{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many thanks to **Eduard Silantyev** (**@eliquinox**) for contributing this Jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = 16,10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use exxeleron qPython to interface with Python:\n",
    "\n",
    "https://github.com/exxeleron/qPython\n",
    "\n",
    "Install it using\n",
    "    \n",
    "    pip install qpython\n",
    "    \n",
    "On Windows you may need\n",
    "\n",
    "    http://landinghub.visualstudio.com/visual-cpp-build-tools\n",
    "    \n",
    "and follow the advice (for x86 or x64) from\n",
    "\n",
    "    https://stackoverflow.com/questions/14372706/visual-studio-cant-build-due-to-rc-exe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<pre>\n",
    "Contract: Crude Oil West Texas Intermediate\n",
    "Exchange: NYMEX\n",
    "Contract size: 1,000 US barrels (42,000 gallons)\n",
    "Margin/maintenance: $2,640/2,400\n",
    "Tick size: 1 cent per barrel ($10.00 per contract)\n",
    "Point value: $1,000\n",
    "First notice date: 2016.05.24\n",
    "Expiration date: 2016.05.20\n",
    "\n",
    "V = 40 means: 40 * (point value) per 1 contract, i.e.\n",
    "40 * $1,000 per 1 contract, i.e.\n",
    "40 * $1,000 per 1 * 1,000 US barrels, i.e.\n",
    "$40 per barrel\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from qpython import qconnection\n",
    "q = qconnection.QConnection(host='217.37.207.169', port=41822)\n",
    "q.open()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = q('select datetime: date+time, bidprice, bidsize, askprice, asksize from quotes where date=2016.04.18, sym=`CLM16', pandas=True, numpy_temporals=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>bidprice</th>\n",
       "      <th>bidsize</th>\n",
       "      <th>askprice</th>\n",
       "      <th>asksize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-04-18 00:00:00.159</td>\n",
       "      <td>39.79</td>\n",
       "      <td>7</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-18 00:00:00.739</td>\n",
       "      <td>39.79</td>\n",
       "      <td>6</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>4</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>3</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>3</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>21</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-04-18 00:00:01.664</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-04-18 00:00:01.664</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-04-18 00:00:01.688</td>\n",
       "      <td>39.79</td>\n",
       "      <td>2</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-04-18 00:00:01.826</td>\n",
       "      <td>39.79</td>\n",
       "      <td>2</td>\n",
       "      <td>39.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  bidprice  bidsize  askprice  asksize\n",
       "0  2016-04-18 00:00:00.159     39.79        7      39.8        3\n",
       "1  2016-04-18 00:00:00.739     39.79        6      39.8        3\n",
       "2  2016-04-18 00:00:01.354     39.79        5      39.8        3\n",
       "3  2016-04-18 00:00:01.354     39.79        5      39.8        4\n",
       "4  2016-04-18 00:00:01.354     39.79        4      39.8        4\n",
       "5  2016-04-18 00:00:01.354     39.79        3      39.8        4\n",
       "6  2016-04-18 00:00:01.354     39.79        3      39.8        5\n",
       "7  2016-04-18 00:00:01.661     39.79        1      39.8        5\n",
       "8  2016-04-18 00:00:01.661     39.78       20      39.8        5\n",
       "9  2016-04-18 00:00:01.661     39.78       20      39.8        6\n",
       "10 2016-04-18 00:00:01.661     39.78       20      39.8        7\n",
       "11 2016-04-18 00:00:01.661     39.78       21      39.8        7\n",
       "12 2016-04-18 00:00:01.661     39.78       22      39.8        7\n",
       "13 2016-04-18 00:00:01.661     39.78       22      39.8        8\n",
       "14 2016-04-18 00:00:01.661     39.78       22      39.8        9\n",
       "15 2016-04-18 00:00:01.661     39.78       22      39.8        8\n",
       "16 2016-04-18 00:00:01.664     39.79        1      39.8        8\n",
       "17 2016-04-18 00:00:01.664     39.79        1      39.8        7\n",
       "18 2016-04-18 00:00:01.688     39.79        2      39.8        7\n",
       "19 2016-04-18 00:00:01.826     39.79        2      39.8        6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFpCAYAAACI6H7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25284718080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "ax = fig.add_subplot(211)\n",
    "ax.plot(df['datetime'], df['bidprice'])\n",
    "ax.plot(df['datetime'], df['askprice'])\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
    "ax = fig.add_subplot(212)\n",
    "ax.plot(df.index, df['bidprice']);\n",
    "ax.plot(df.index, df['askprice']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['midprice'] = .5 * (df['bidprice'] + df['askprice'])\n",
    "df['dmidprice'] = df['midprice'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>bidprice</th>\n",
       "      <th>bidsize</th>\n",
       "      <th>askprice</th>\n",
       "      <th>asksize</th>\n",
       "      <th>midprice</th>\n",
       "      <th>dmidprice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-04-18 00:00:00.159</td>\n",
       "      <td>39.79</td>\n",
       "      <td>7</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "      <td>39.795</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-18 00:00:00.739</td>\n",
       "      <td>39.79</td>\n",
       "      <td>6</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>4</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>3</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>3</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "      <td>39.790</td>\n",
       "      <td>-0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>6</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>21</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>9</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-04-18 00:00:01.664</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-04-18 00:00:01.664</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-04-18 00:00:01.688</td>\n",
       "      <td>39.79</td>\n",
       "      <td>2</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-04-18 00:00:01.826</td>\n",
       "      <td>39.79</td>\n",
       "      <td>2</td>\n",
       "      <td>39.8</td>\n",
       "      <td>6</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  bidprice  bidsize  askprice  asksize  midprice  \\\n",
       "0  2016-04-18 00:00:00.159     39.79        7      39.8        3    39.795   \n",
       "1  2016-04-18 00:00:00.739     39.79        6      39.8        3    39.795   \n",
       "2  2016-04-18 00:00:01.354     39.79        5      39.8        3    39.795   \n",
       "3  2016-04-18 00:00:01.354     39.79        5      39.8        4    39.795   \n",
       "4  2016-04-18 00:00:01.354     39.79        4      39.8        4    39.795   \n",
       "5  2016-04-18 00:00:01.354     39.79        3      39.8        4    39.795   \n",
       "6  2016-04-18 00:00:01.354     39.79        3      39.8        5    39.795   \n",
       "7  2016-04-18 00:00:01.661     39.79        1      39.8        5    39.795   \n",
       "8  2016-04-18 00:00:01.661     39.78       20      39.8        5    39.790   \n",
       "9  2016-04-18 00:00:01.661     39.78       20      39.8        6    39.790   \n",
       "10 2016-04-18 00:00:01.661     39.78       20      39.8        7    39.790   \n",
       "11 2016-04-18 00:00:01.661     39.78       21      39.8        7    39.790   \n",
       "12 2016-04-18 00:00:01.661     39.78       22      39.8        7    39.790   \n",
       "13 2016-04-18 00:00:01.661     39.78       22      39.8        8    39.790   \n",
       "14 2016-04-18 00:00:01.661     39.78       22      39.8        9    39.790   \n",
       "15 2016-04-18 00:00:01.661     39.78       22      39.8        8    39.790   \n",
       "16 2016-04-18 00:00:01.664     39.79        1      39.8        8    39.795   \n",
       "17 2016-04-18 00:00:01.664     39.79        1      39.8        7    39.795   \n",
       "18 2016-04-18 00:00:01.688     39.79        2      39.8        7    39.795   \n",
       "19 2016-04-18 00:00:01.826     39.79        2      39.8        6    39.795   \n",
       "\n",
       "    dmidprice  \n",
       "0         NaN  \n",
       "1       0.000  \n",
       "2       0.000  \n",
       "3       0.000  \n",
       "4       0.000  \n",
       "5       0.000  \n",
       "6       0.000  \n",
       "7       0.000  \n",
       "8      -0.005  \n",
       "9       0.000  \n",
       "10      0.000  \n",
       "11      0.000  \n",
       "12      0.000  \n",
       "13      0.000  \n",
       "14      0.000  \n",
       "15      0.000  \n",
       "16      0.005  \n",
       "17      0.000  \n",
       "18      0.000  \n",
       "19      0.000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6wAAAJCCAYAAADEAkoCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8lfX5//H3nT1I2BskTJEhKENxgRvFXUettdXWqq3d\ntf6wjlonHdp+W+2wVq3W3VatRsUBKCCyZO8VCISREELIzsm5f3+cnJMz7rNnktfz8fDhfd/nHhfB\nHM91Pp/PdRmmaQoAAAAAgFSTluwAAAAAAACwQsIKAAAAAEhJJKwAAAAAgJREwgoAAAAASEkkrAAA\nAACAlETCCgAAAABISSSsAAAAAICURMIKAAAAAEhJJKwAAAAAgJSUkewArPTq1cssKipKdhgAAAAA\ngBhbuXJlhWmavUM5NyUT1qKiIq1YsSLZYQAAAAAAYswwjN2hnsuUYAAAAABASiJhBQAAAACkJBJW\nAAAAAEBKImEFAAAAAKQkElYAAAAAQEoiYQUAAAAApCQSVgAAAABASiJhBQAAAACkJBJWAAAAAEBK\nImEFAAAAAKQkElYAAAAAQEoiYQUAAAAApCQSVgAAAABASiJhBQAAAACkJBJWAAAAAEBKImEFAAAA\nAKQkElYAAAAAQEoiYQUAAAAApCQSVgAAAABASiJhBQAAAACkJBJWAAAAAEBKImEFAAAAAKQkElYA\nAABEbGd5jS5/arGO1jcnOxQAHRAJKwAAACJ25Z8/15rSKs3ffCjZoQDogEhYAQAAEBHTNF0jqyWH\na5McDYCOiIQVAAAAEdl7pN61/ad525MYCYCOioQVAAAAEWmxm5bbABArJKwAAACIyAPvbEh2CAA6\nOBJWAAAARKR3l+xkhwCggyNhBQAAQEROHNTVtZ1mJDEQAB0WCSsAAAAiUpCT6dq+bsrgJEYCoKMi\nYQUAAEBE3FvZVNQ0JTESAB0VCSsAAAAikpuZ7trOcdsGgFghYQUAAEBEbG6tbN5ZU5bESAB0VCSs\nAAAAiIid3qsA4oyEFQAAABF5/KOtHvstJLAAYoyEFQAAADFR12RLdggAOhgSVgAAAITNKjllgBVA\nrJGwAgAAIGyfbDrk2r5m0qAkRgKgIyNhBQAAQFSG9+kiSXp3bZmKZhfrYHVDkiMC0FGQsAIAACBs\nhtG2/c/PSyRJ97y5XpJ07d+WJCEiAB0RCSsAAADCtvtwnWv7SF2Tx2t7j9QnOhwAHRQJKwAAAML2\n27lbXNsNzXaP12hvAyBWSFgBAAAAACmJhBUAAAARmz6qt7pkZ3gc894HgEiRsAIAACBsF43rJ0l6\n/uYpev9HZ3q8NmFwV9mZFgwgBkhYAQAAELb31x+QJBmGoYIczxHVxdsP64ZnliYjLAAdDAkrAAAA\notItL8vn2JKdh5MQSeJRYAqILxYYAAAAICymSZImSUWziyVJ3fMyter+C5IcDdAxMcIKAACAsDCo\n6OlIXXOyQwA6LBJWAAAAhMVmt/scs6oM/OKSEl325KIERASgoyJhBQAAQFic6zZnje/vOjbvZ9M9\nzrnkxP667+0NWrv3aEJjA9CxkLACAAAgLHuP1EuSxg4sdB3rXZDtcc67a/e7tt9ZU6YXlpQkIjQA\nYbryz4tVNLtYtY22ZIdiKeSE1TCMdMMwVhmG8W7r/jWGYWwwDMNuGMbkANeVGIaxzjCM1YZhrIhF\n0AAAAEie+95aL0lasKXcdcwwDP3gnBGW5//glVW6/+0NCYkNQHhW7amSJM3bfCjJkVgLZ4T1R5I2\nue2vl3SVpM9CuPZs0zQnmqbpN7EFAABA+3C4tsmx4VV86WcXHK+8rHS/1zU0t8QxqsSye1WeKq2s\nS1IkQGz8feHOZIdgKaSE1TCMQZJmSXrGecw0zU2maW6JV2AAAABITUbrv6sbfKvjPnHtBL/X2TtQ\nO5xmr8JTZ/5mfpIiAWIjVdebhzrC+gdJd0nyLQkXnCnpY8MwVhqGcau/kwzDuNUwjBWGYawoLy/3\ndxoAAACSLCfTMYo6oFuuz2szx/X3OeZ0tL7jtH+prk/eer/VpVUqqahN2vPRcaVij+WgCathGJdI\nOmSa5soIn3GGaZoTJV0k6Q7DMM6yOsk0zadN05xsmubk3r17R/goAAAAxNupw3pIki6fOCCs66Y9\nNi8e4STF9X//ImnPvuKpxZrxuwVJez46rlTssRzKCOvpki4zDKNE0quSzjEM41+hPsA0zX2t/z4k\n6U1JUyOIEwAAACmgyWbXrtbRvTNG9Ar7+vqmjrGOdfuhGknSXTOP93vOxrJqVwsgoD1IxWn7QRNW\n0zTvNk1zkGmaRZK+KmmeaZpfD+XmhmHkG4ZR4NyWdIEcxZoAAADQDr2wpEQfb3JUE81ID79D4g9e\nWRXjiJJr8pAeru31+9rWAK7be1QX/3Gh/vrpjmSEBUSkrKo+2SH4iLgPq2EYVxqGsVfSNEnFhmHM\nbT0+wDCM91pP6ytpkWEYayQtk1RsmuYH0QYNAACA5NhYVu3aTjMCnOjH4u0VMYwm+SYN6e7aXrar\nUo02xwhyyWHHKPTq0irL6sjr9h5lHSpSTqWzAngKCSthNU1zgWmal7Ruv9k68pptmmZf0zQvbD1e\nZprmxa3bO03TnND6z1jTNB+J/R8BAAAAyZAZwQhrfQdqbSNJ6W5Z+4PvbtTY++dKko7UOT74f7Tx\noEbf5zleU1ZVr0ufXBTROtTVpVWRBwsEkYpT2CMeYQUAAEDn4/5x1lkt2NvCu852bWdleH7cDNSn\ntSOwtX7gNwz/w887ymsivv+qPUcivhZwWl1apaLZxbr2b0s8jqdiL9aMZAcAAACA9mNPZV3Qcwb3\nyHNtN9k8uyLWtfOiS0PvLlYodWnue8t/2ZYVJZEnnSk4AIZ26OONByU5prG7m7vhYDLCCYgRVgAA\nAISsR35W2Nc8feOkOESSHFbJ6v99dWJY9/h0a3nEz5+/+VDE1wJOczccsDye62fWRDKRsAIAACBk\nkRQKumBsP4/9uiZbrMJJqO2Hjnnsn3RcN0nStOE9g17rXnipICfySY6L3IpWFc0ulp0hV0Rg2yHr\naempuMachBUAAAAhC3eE9T/fPU2SdMGYvq5j0UyJTabHP9zqsf/0jZMlSVkhFJ/a5ZboX+iVwEej\nLgUTDCCWSFgBAAAQN862L09/Y7LrmHchpvbCWfnXqXdBtiTfP8+/vtjtc63dbS7xtoNtI7VmKAti\nA0jFqq6h+vOC7Vq5u31+edGeea9bTXXt890CAAAAKS0rI02XThhg+VrfwpwERxMbFTXWPSq91/3d\na1FwKSOt7WP3P5e0JbT7quqjiqk9J6y/+WCLvvKXz5MdRqfjXRk41VElGAAAADG39eGLfI499bWT\ndcfLX2pXRY2G9spPQlTBrd1bpcraJs04vo8kafZ/1urV5aV64/ZpHutQ3QVqYePk3q/VXThVkx//\ncIvPsUv+uFBlRxs0c2w/1Te3qMVu6tmbpqT8KHa0I8uIn9LKOo9K38mW2v8lAwAAIKVs3F8d8bWH\naxslSd96fkWswom5y55crJueW+7af3V5qSTpmr8u0UXj2tae+ktA/fFu7+P0yrI9Id/jT/O2+xwr\nO9ogSfpgwwF9urVci7ZX6NnFu8KKLRlKK6MbWUb8XPHU4mSH4IGEFQAAACExTVPHGhwVfr9z5tCw\nr++Znx3rkOJm8sMf6fUVpR7HnIOCGx+8UDsevdjjtfl3zgh4v/pm68rIVXXNEcfoz18W7ND6fUdj\nft9YKq9pcG0z2ppaDtdaT31PFhJWAAAABFVWVe9RTOieWWPCvse4gYWSpBF9usQsrnipqGnSXf9e\n63HsmUWOkUurqsDOAkz+VLcm+tu92on4G3mNxtH6Zl3yp0Uxv28s/WXBDte2988ZcEfCCgAAgKBO\nmzNP9729Iap7DOnpWLd6ytAesQgpaaymA3fJDlwaJq11nWuZV5GljzYdjF1gXlK5Au+m/W2Vkt9Y\nuTeJkUCShvRMnTWr3khYAQAAEFCJWw/RWHhpaejrNhMp1NHOUIosebO1OO79mtc04yabXUt3Hg56\nfSTTZr/yl8/9FopKtur62E+FRuTOO6Fv8JOShIQVAAAAAZXXNHrs/+/7pycpkviqabReZxqq/zdz\ntM+xf3zT0X+2ucWRcG6yKFq1K4QvBLy71/zy0tCmZKdK2xvTNNXcYpetxS673VQEOT/i6BcXn+Da\nzgizoFi80dYGAAAAAXl/fi3IyUxOIHEW7ajf0F6+0yr7d82VJP3qnQ2aOa6fdpY7ktOC7Awda02Q\nQ8kptxw45rE/uHvqTuG08tC7m1zVi88c2UveA8amaUY0co3YcJ/mbkuRLzmcGGEFAABAQN7JRTQj\nMHlZ6Snbg9Vmj64A0oVj+3ns3z59uEw5fnj7jzZ4vFbT1Daam5eVHvTe76wtc21fN3mwMtLb/g5O\nHNTV73Wpkny4F+xauK1C104Z7PF6fYpOXe5MUrUYGgkrAAAAAvIe+XJPlsI1cXA39eqSFW1IceGc\ntuvNqiqwFcMwPKbqDuyW45Hs3/iPpa7t0f0KXds/fm110HuvKKl0bfcpzFZZVVsC/Nb3/E/RToUp\nwcVr96upxfPLgCabXd3y2kbqP1h/INFhwUu/wpxkh2CJhBUAAAAB9cz3TDAz0iL/CJmeZvhNDJPN\n1hrXZRMGeBx/5dZTXNt/ueHkgPe4fOJA1/b1U49TTmbbz2rhtgrX9n++Oy2s2M4c2du1nZuVroXb\nyl37aQFGvG0tsW+bE647Xv7S51hzi13ZGW0/mznvb05kSHDz6q2nSpK+3JOaVaVJWAEAABCQd3oZ\nzZTghdsqtLq0So221JsC+slmR4uZacN7qmTOLNc/PfLbeqzOHNfP3+WSpK65baOGGelpyvQzOpuT\n4TkN+NvPL5c9wGjoO2vapgTnZqYrP0gbHac/u/U7TRarNkCvLi/VwepG3XnBKEnSoWONPucgMU4d\n1lOSVNeUer+TEgkrAAAAgvBuqRJqshSIex/OVPGHj7dJku7+7zqP48f1aCtwFKwwkHdy5n6tu7Q0\nQ30L2xLhTzYfCriOc9uhGkmOdYbXTz1O01qTDG/DeufrkSvH6dzRfSRJz39eEjDeRLjx1CEBXiuS\nJF09aVCCokF7Q8IKAACAgLzH/bIyov8I+bu5W6K+R7zkexVBshohDFWgBHf+nTM89sf+cq6KZhcH\nvN/fbpyknMx09S7Itnx93s9m6IZThuiyiQMsX0+GowGqL3dtXce6ZEfwXrRInEj6/sYLCSsAAAAC\ncv/s6j7lNRqLtleoyZb89ZXuzmkdlXzrDt8iRoN75Ma8P2VuZvDqwN6cMYzuVxDTWOJpZ3lN0HP2\nVdUnIBKE6kB1Q/CTEoQ+rAAAAAjCkbH+8fqTfAoSdSQlhx09Ukf29U0GF951TsyfF2x6cYvd1D8/\nL9FVJ7cVcvLXpqZkziyPfWf/12RZU1ql5ha7Jhf1CLm/6o7yGn2x87Cun3JcwEJSiL/qepv6+++W\nlFCMsAIAACAksU4hUqHlirud5bUxuc/1Uwdr4uBuUd9nyY7DevDdjXpy3nbXsb6trUecU2l75Fu3\nCHJvPbRpf3XUsYTr8qcW6+q/LpEknTK0R0jXnPv4p7rnzfUePWeROLdNH+bafv7zXUmMxBMJKwAA\nAAKK13K2I3VNOuG+D1RZ2xSfByTJY1ed6DGt+Ozjewc425dz/WBNo02S9MyituShS2vBq+yMdJXM\nmaUv7zvf8h5pbqOayaz+WttoU0OAYlJWqhtscYoGgdx90Qmu7d2H65IYiScSVgAAAATkzFftMchc\nJw3p7to+bc481Te36OSHPor6vqls/pby4Ce5+cunjlY0zVH0UHUvmpPMAjo/eW21/rlkd1jXNIaZ\n4CJ0tY2hfRngb+p5MpCwAgAAIKBluyolSa8tL436XvfMOiH4SQlWVdekjWWJmTa7/J7ztOPRi137\nn/38bJ9zFmx2JLjRTOV1r+Sc6PWg5W49VT/ceNC1veXhmdrp9mf3pybEpArheX7xLp/f4dOG97Tc\nT6Xp+hRdAgAAQED3vrVektQYg6q+vbtYt2NJhppGm7pkZ2jig/Ed4Z1a1EPLShxJv3c7muxM3/Ej\n57l/XrAj4mf2zG97jj3ByceURz62PJ6VnuZRgKmnn/W3f/h4m3583qi4xNaZPfDORp9jz9881WPf\n+Tse6khsIjDCCgAAgJB858xhwU8KYnCPPJ9jXzl5UNT3DdcH6/dr3C/nau3eKo/jN51WFPNnvXbb\nqX5fy0wP/eN4v9aCSyGd27Xt3ETmq4HWqzqT1e/NGK5bzhiqT+9yjC4P6en73wRiy9+XFt49lU8d\n5iiQNah76vydkLACAAAgJP4q0kbrP1/ulSTd+sIKFc0uVtHsYn3r+eVxeZaTc6rqZU8u9jgejwJQ\ngdq6ZLcmDJd6tQt64H8bfM49c2SvsJ770BXjJEn//LwkrOv8+dbzy1U0u1hvr95n+XplbZNG3/dB\n0PvcNXO07r1kjKuA1LgBvv1TjjU0RxcsPFz+1OLgJ0m69azhkqSPNx0McmbikLACAAAgoPPH9JUk\nTXYrmBSN/33/dMvj7usd520+FJNn+ZOdYf0xeFdFbFrbePu/r07UG7dP8zmen52hf35rqh68bKy+\nN2O46/jzFknmY1eND+uZzqWrxev2h3WdP86/k/+ttm47s6uiJqL7/ubqE32OdbTK0cm2bt9Rn2ML\n7/JdP51jMUU92VIvIgAAAKSULtkZGtwjN2bFe04cFH2P0mg0NLfolWXWBaTGD/Id7YuFyycO1JQi\n636k00f1Vvf8LI0bGPjZGWFMH5akop75bduzi/Xa8j1hXe/P/qMNlsfLqqyPB5Of7VtWJy3AqDTC\nc8+b6yyPW03Pz0xLvfQw9SICAABASmmxm8pIwQ+ykSqt9N9j8uY4rGEN1cyx/WJ6P+8KsP/vP9aJ\nS7h6drGeGh5oqexHPzkr4D2vnzrYY598NXZeWhr6FxWJrigdio7zzgMAAICYa7S16H9rygImebFQ\nvNZ32mq8+oc6+5xa6ZnEKsZWycLDretQI+G9dvaSE/tHfC93C7dVWB4P9Pc1sm9BwHs+dtWJKpkz\ny7Vva0mdtirRKquq120vrlBdU+pU3g3Fy16JrnN9eaKRsAIAAMAvZw9WW5xLzd7x8pc+x+LVj/O/\nX1oXDZKk7nmZcXlmpK6ZPEgXjevnWkccjXingM0xSDLTW5P2tRZrLturX3+wWXM3HNTcDQeSHYrL\n6vvPD3rOL9ymEiezLysJKwAAAPyK0yCnrp7U1spmYLdcy3Ni+SH5YHWDimYXa/F269FBp0AVfZMh\nOyNdf/n6JP39G5Ojvlfx2v0xG7V++N2N2lVRq6q6Jj3x0VZtOXBMDxf79vkMl7MY1g9fWRX1vVKF\nczr95v3HkhxJm255/it+Z6b7/g58ueeIazvRfX1JWAEAAOBXhsWH11j48XkjXdv+kqimFnvMnucs\nPHPDM0tjds94uOPs4cFPisLeI/URX+s++vzMol26/+31evDdjfrjJ9t04R8+U1WdoxXNr78yXueM\n7qOHLh8b9jNuPSv6Xr+p5tOtjurKf/tsZ1Kef8aI8NohWY2U17rNdvjOCyuijikcJKwAAADwy1/7\nl2gN6p6nkjmzdN3kwSrzU3X2tWWlao5R0upMplLdlScNTHYILlsOHNO5jy/QfW+tl2maKsz1nC69\n+3CdDlU3+lx33ZTj9OxNU3TjtCKVzJnlsTY1mDH9C6OOO9VMcmsH9enWcpVVRf6lQSRG9OkS9T1e\nX9FWVfuTzYe0YEt82065I2EFAACAX/EuflMboBDN4x9t1V8W+C+QFI7TwxxlSpb+Xa2nR8fKsYbQ\n1wVf+IfPtKO8Vi9+sVsLtpS71pc6mXFYFTs2SGuf9mjuhrb+wt98dpkue3JRQp/v/aXP1KHW7ZUC\neW+d5/rbm55bHlVM4SBhBQAAgF9lR+M7GrT5QOB1fe5r59w12ex6cUmJTNPUKotzahtt2nyg2rXv\nrxVLqrHqSRpLkS7R/dGrq3TQayT8cE2Tusa4SFXvJFZpTpSKmqaEPs+92NN5J/TR67dNS+jzoxXf\n3wgAAAC0az95bU1c77/9UE3A1xdsKbc8fvx978s0HT0mNx84pqdvnKQL3PqYfv/lLzV/S7k2PzRT\nOZnpuv/tDUFjOSHFpqP2Log+eZs2rKeW7Dzs2o+0kFW1xchsXVOLNpZVW5wduYwU7APa3rknyOee\nEH216URjhBUAAABBLbvn3IQ8Z8nd5+ilW04Jep6zTpNzhHb3Yc8+sZ+19grdfqjGb5J22YQBru3N\nD83U/75/eiQhx81tMShA9OK3p2rzQzNd+7UhtgoKtZrw/hiPwFv1om3vbjqtKNkhSJI2/OpCfXXK\n4LCuKa2s09Ekr/8mYQUAAEBQ3XITM6W2f9dcHdcjL+zrHnlvk8e+M0m95E+L9LW/f2F5zeSitmI4\nOZnpykxPrY/GQ3rmR32PjPQ05WSmu/ave9r6Z+Ht3Cc+Dem8tBRrAwT/8rMzwm7bdOZv5mvCgx/G\nKaLQpNZvJQAAAFKSVW/GeBkcQcIayNJdlZbHvzGtKKbPiZWV956nv379ZJ0/JnbTN++/ZExY5+8s\nrw16zpkje+nUYT0jDcmvbnmZMalsmwr+t6ZMz39eEvQ8u92MWY9cKwU50a0EHdY7+i9PIkXCCgAA\ngKDCHZkJ1dWTBrm2+0S5ZrNodrEk6czfzIvqPsnWs0u2Zo7rH9N7ZobRnqi0si74SZIWbqvQvM2e\n7U3ys9L9nB26KUU9lJFm6OSHPlLR7GK9uKQk6nsmyw9fWWV5vGh2sW75Z1s/0++8sEI/fT3268Wd\nvxP2MNYujx3gu5Z7Z3mt+nfNiVlc4SBhBQAAQNL89uoTNXNsP10xcYDm3TnDdfyOs4dHfM/SyuDr\nKt+6w7Fedcnd5+iN29tX1dRIZIcx3dlfZWanQD+v12Pws8zKSFNTi12VtY5iQb96Z2PU90xFH29q\na3fzyeZDenPVvrg9q7apJeRz377Dei2396h3PEeE3VElGAAAAAEN6xW/6YCGYeivN07yOf7zC0dr\nZ3mtPtl8SIeONehbzy9Xdb1NN5xynG6b7j+ZraoL3jLktunDNHFwN0mONbPx7n2aCrIzQ09Y73wj\n8EjflCL/fTzHDoi+j2pWeprHlGRbhJWN24vPd1QkOwQPGelpunbyIL2+Yq/H8ZF9CrRwW1usphl5\nm6RwMMIKAAAAvwZ1z9XE47ol5dlH6prUu0u25m44qPX7qrWnsk6Pvb854DWf7zgc8HVJenbRrliF\n2G5ccuKA4Ce1am7xTRB/85UTJUmL/t/Zfq979qbJ4QdmoSbESsbtQc/84MXK5gT5bzoZMixG5Btt\nnqO0LQkaYSVhBQAAgF+2FjNpvTF7dsnWvqp6vbOmLORritftD3pOl+zON8kw3e3v8JvPLgvr2n6F\nObp2ymCVzJmlQd2tC2KVzJmlc0bHpkhUjUXPV1uLPSb3TrRRfQuCnrN279GYPGv34VoVzS5WRU1j\n1Pey+p3PzvBcn1x+LPrnhIKEFQAAAH7Z7KblaEsiFK91JJ/L/FT5DXSNlalDHVNZvxOD/qbt2adb\ny0M+9+bTi/T8t6b4HC+IY9K/ZKfvKPmaGCV1ibZid+j/7UZr+m8XSJKueGqxz2tv+VmX6k9Gmu/v\n/ItflOihy8e69v80b3t4AUaIhBUAAACWbC12VdQ0qtmWWqNbkY629erimJ7ZrzA51U5TyTMLd2rb\nwWN6dtGugMVzfnnpWI3u51s19qxRveMZng9/bZVeWFKi/UeDF9lKFqvp1YF4r8F+8YvdIX9h42yD\nlJeV7vOlhHPNdqgabL5FmppbTN04rcj1e5SookskrAAAALD09mrHVNw3Vu4NcmZ8+Fv/t2J34Cq2\n/nx3+giNG1iY8GQrFT1cvEnn//4zPfjuRu0orwn7+p9feLxre2C3+BetssqNDhxt0P1vb9B3Xljh\n+2I79a7XDIH73lqvm54LbQr3nsOOdkRbD9aEPe3b28tL9/gcu/OCUZKk6tYp240J+iKLhBUAAACW\nVpVGlhjGSh8/I6FfffoLj/0Jg6wr014+0bPQ0NgBhXr3B2eqV5fo+r22V//69imWx4/Wh1/kqKhX\nvq6d7Oih+8NzR0QVVyjSLdZUNjQ7RgGdiVoqO31ET59jUy2qLbuPFje1JoR1TS1BRzNN09SWg8d8\njsXS8N6OtjbOfsnhTC2PBgkrAAAALP3rC99RlkQ6Uhu8RY0kndDfd8qqJN3qtVY1LUnFo1LFqL5d\nLI//4OUvExxJYFZtlNIs+qfUhdFbNNm+OuU4n2MZFtOc3XPMA0cbXNvB1vC+t+6Az7Ghd78XRoTB\nObsLXT/V8WepDPH3M1okrAAAAFBzi13bDx0LfmIC9esa2lpTfyOxkqN67c5HL9b2Ry6KVVjtlr+f\nU5lbYuQ068T+KpkzK+D9nMlVrJcy3jVztM+xw7WN2nukbST16c92uEYjqy2qCidai93U/328TWv3\nVnkcnzi4m8YNLNS04b4jrFYJ37p9R1Xf1KKSilqPNjKHqn3/jtwdDPJ6LNjsjhHfmeP6xf1Z7khY\nAQAAoIfe3ajznvhM+6p8C9j8+LyRSYhIWl1aFfwkSSf76RPrnPqblmYkrdJxe+P80iJQtWWnGcf3\nkSSNGWA9wh2pU4b6TpW98R/LdMav50uSPlh/QI++t1nf/mfqrF393Ydb9PuPt+qyJz0r9B5raFZt\nY4vys3yrKh9rsPkUEFu4rUK3/2ulZvxugcca0T2Vgac9P/juxiiiD82wXo4R+nq3kW27Pf6Fl/jN\nBQAAgF5YsluStP2QowDP+n1tUxB/eE5yEtZg7p11gpbdc64rcXK36r7z1ZdqwGFz/v2H4uLx/bTs\nF+fqxEHhVaANlcUsYB1raNaCLYd8jieqYq0/y/1U8jUl9e+ao9ysdM372XSte+AC12vZGWlqsqh4\n7VwbWt3Q7DoWqHfw4Rj0XfXn4SvGubbHt64Vb3FLUlsS8HMnYQUAAIBLS+u0v0v+tMh1LFXXfja1\n2NWnwDr1krx1AAAgAElEQVQp7e6nwjACu/1foa9nNQwj4HTsSKW3ru28cIzv1NPxD3yoV5eX+hyP\n9XrNcK1ymw3Q7JaE7iyv1ec7HH1lh/XuooKcTNdrTS122QKMUH7t70td2/7O23bwmCY9/HHA2CJp\n43TLGUMlSZOGdPd5rYfb71YkFabDRcIKAAAAFfXMk6SUGpXc/NBM1/ayX5yr52+e4vF6lp9pvu/9\n8My4xtWeDbUoaGQl3L6dsVSYk6niH56hP3x1oj76yVlJiyMc7qOOTUHavfz9G5M1YVBXNdnssrX2\nab3j7OEBr/HXeziUafNH6sIvjjT7otF6+47TdUL/Qn368xlafs95rtcGdW9rY7TlQPzXvZOwAgAA\ndHKllXUqaW0N8mWEPU7jIScz3bXdpzDHZ+pvYW6m9yWSYr+msiP52lTfarWSVDS7WE98tNW13z/E\nglfxMnZAV+Vkpqt/Anq8xkJOZltatW5f4Iq+54/pq/zsDB061qi3V++TJO2qqFV+Vrrfa/yNsGZl\nBE/nbjhlSNBzvGWkp2lC65cWQ3rmq3dBWysow22u9jI/U6FjiYQVAACgk/ti52HXtvd0y55Jnlr7\n2FXjde+sE1z7F47tq9H9CjSqbxddffIg1/FlvzhXkjTnqvEJj7E9ufn0Iv3umgn6+KfTfV774yfb\nXNuZKVKkKiPE6ehjk/wlxR0z2nrRvru2zOO1iyyq6jqnCf/qHUexpPfWHdBzN0/1e39/CeuIPtat\nitz96Nz4rUF/Y+XeuN3byf/qXQAAAHQKxwK0Bbl60iC/ryXC9V4jgn+7cbLleX0Kc4K2YYFj5Mz5\nd1oyZ5au/svnWmExqu6ssJxs6SEmrLWNkbW2+XRruV5cslt//8Ykj5HDcL25ap9ru/yYowiScxrv\nyL4FQa/vV5jjMaqdkWZ4JKlz3t+sb0wbooeLN+nlpXu08K6zNbhHnrYeDD4lNzszfl8+JKLYVWp8\ndQIAAICkyXObivjN04o8Xvv5hccnOBokUlV9s+XxeI7KhSPUkd6xA7tGdP+fvb5aH286GLD4USh2\nVtS6tj/aeFCSVNOaRNc3BU+m7754tAa6TX+2WkO8urRKLy/dI0n61xeOqt6Pf7jV5zxvUeThQf3y\n0rHxu3krElYAAIBOzr1H6WetLTUkaXS/AvqXdnBVfgryFOamzkTM/37vtICvj+5XELTQkT8VNY4/\n/8oYrt125r7OJHhwj7yg1xzfr0BpaYZGtk7xzbdoY/PSF3tc23/7bKeKZhdr7xHfvsne0uOYsWaH\nsIY2WrwDAQAAdHLu6wTfXbvftb05ARVAkVzOhM1bNNNjY218kNHT7Iy0iBNWp68+/UVU17tzjo46\nKwBnpAVPuZwjybmtsx1MSQ9dPla/vHSMa3p28br9/i53KchxJLoL7zrbdSwtjn+X0Y5MhyJ1vjoB\nAABAQjU0t2j0fR/4HC+tdFQMTnalWEByJHMXjOmrD1un2robP7Cr9h9t0Jq9gSvzJtLq0ir9/I01\nun2Go1WN1TLcAV1zVHa0wbXvbNF0xcSBWrv3qI7WN+vGaUWSpOP7Fuhrzyz1vYmFdQ9c6HMsnt89\nJCJhZYQVAACgk/LXfuPM38yXJO13+0ANJNN3Z3j2Kb10wgAN752vP15/kg61FjmKZpS1wGIKbjTe\nWLlXf2qtuvzW6n0+r//m6gke+xnpjqwys/XfG9x+N9NCLDzlTzxGyy85sb8k//1hY4kRVgAAgE5k\n3d6jmrvhgO688Hg1NLckOxwkmXc12lR10nHdg1aBboniz3HHOSOCnxQm53TrqjrfwlZnjOzlsZ/b\n2nO4qFe+JOnUYT1drwWb0tslO0M1jTate+CCqOINx6NXjde7a/drQ1l13J/FCCsAAEAncumTi/Tk\n/O06UtsU9bo/tH/tIVkNlc0e+X/P0SS7/jiLLV06YUDQc7NaixeN6NNFGWmGvnZKWzunol6Bizb9\n9PxRys5IU05mesDzYslZbOnfCejDSsIKAADQCf34tdU6XNtWcOfeWSfojBG9XCM96LzaWz/bey4+\nQZIUSb7ar9CxTnv/0XrtPlwb5Gz/BnbL1Vmjensca2kN6IqTBga93ll0qX/XXG18cKYuHt/f9Vqf\nAv9ryXc9drG+dcZQrXvgwpBbAMVCdkbi3idIWAEAADqhT7eW665/r3XtpxmGFm2vUL3bNOErQ/ig\nDSTbq8sd7V42lIVfeMm5PPRfX+zR9N8u0PKSyohisNntGuBVpOz1FY7RR+e61EDcK3VnhdEqxrk+\n1eqaaycPCvk+qYyEFQAAoANqstnDmvJr1SvyiWsnWJyJjuTbZwxNdghR21HuGBndcjD8Nkw1jTaP\n/e2HaiKKoa6pRel+iiNlp1uPRrp/IRSPwkgPXj5OS+4+J+b3TTQSVgAAgA5o1L3va9S973sc23uk\nzu/5eyp9X0ulXpyID2eRH6drJrW/UbnBPXIlOZLGcFQ3NKu6wTNhfWNFadjPLz/WqGMNNr20dI/l\n6/7asJ4ytEfYz3KXkxk4lcvJTFf/rrlRPSOQEX26qKhn4PW1sUDCCgAA0ElsDTACtXl//Kt9IvXc\nMPU4vXTLKdr04EzdecEoPXbV+GSHFLYnrp0oydGvNBxH3NZwO325pyrs5++pDLz21V+V3+umDA77\nWe7m3zkjquujNaRHnrrkxL/pDAkrAABAO1c0u1hFs4td++4VTx97f5NrO8PfUI+k43rkqUd+VnwC\nRMpKSzN0+oheys1K1/fPGamMBBbuiZUurT1Ub3lhhVaXhp9wRqvJFrjCsL+pwpHOYJjaOjIbqBhT\nInyy+ZDW76OtDQAAAAIwTd8Py3VNbdMc//bpTtd230LrD7jDeuXr9hnD9fvrJsY+QCDO3IsaPb94\nV8jXNbf4rvG+6uTwC431LsiWJP3i4tF68PKxPq8Hajfz8wuP1yNXjgvreY9fM0G/u2aC30S4oyFh\nBQAAaMcq3aY1HqxukOS/cIxztPVvN07yOD7vzhnKTE/TiD5d4hQlED/u39k0WSSh/lit286KYIT5\nF2+ukyR1y8vS1WGuAb7j7BG64ZQhYV0zuEde2M+JN7vdVNHsYt383LKY35uEFQAAoB3be6Tetf3M\nQsdo6j1vrncdu2hcP9f2gi3lkqRsP20z3NtyjB1QGNM4gXhxVgmWpJrG0AsvPf3ZTp9jVtWyg1m2\ny9EKZ3VplfKyMnTTaUVh3yOYN26fJkk674Q+Mb93pAZ2cxR0sttNVdU3S5Lmt77HxBIJKwAAQDvm\nHFWVpCN1jg+NG90KKL2//oAue3KRxzUDullXDnVfU3fmyN6xDBOIm2nDerq2P9tarn1V9QHObrOx\nzHf95YcbDri++AlXc2sbqbsvHh3R9YFMKeqhkjmz9Mw3p8T83pG6YGxfSVJjGO2zIkHCCgAA0I65\nJ5nr9x21PGftXs/j7tMe/+C1bnXW+P6SpFvPGharEIG46pqX6bH/6Hub/JzpybuljSSt2XtUDxeH\ndr23N1ftkyRlZ/hfs9qRPLe4RJL0+Y4K2S3W0sdK/OsQAwAAIG5a7G2jGwdaR1vzs9JV69WT8u9u\n0x/zstJVMmeW5f2euuFkPRWHOIFEKV67X49e0eyTyDqZpqmf/3ttwHuYphl2FV+bPX5JWyprtNll\na2n7s7+2fI/OPr6P+vgp8hYuRlgBAADasWa3D4pVrVOCr7Xo7/iI26hTZjtsXQIEMrx3vsf+5Ec+\n8nvuou0V+vfKva59qzWnLREkn3+6/qSwr2nPnBWVB3bLlc3ti7P/9591+tW7G2P2HN6tAAAA2jH3\nD9a5re0zdh/2rX7qLstP0SWgvfrkZzM89t2/yJEcI6ZPztumo3XNOtpaIMhp0pDuPvfbUFatI24V\nuENx6YQBPse6+xnl7QjOP8GxhnXVniMeI6ySYy2x08HqBu0st65cHgrerQAAANox916SN51eJEma\nt/lQwGsC9YUEOgLvHqVvrd6n3324VRMe/NA1E8HphP4FPtdf/tRiXfHnxUGf497z2Mr3zxkZQrTt\nk3OmxgPvbFRNo+fP4Zjb+uBTHv1E5zz+acTPYQ0rAABAO2O3m3rio606oX+hxwdDQ/KokPqNaUP0\nwpLdPtd7f5gHOprLJ3qOdq7f11YR2HuGwdBeXbTl4Zm67E+LteXgMdfxYDMVJP9Th3c9drGO1jer\nW15WOGG3K+npbe8jVpWCTdPUoWONUT+HhBUAAKCdeeKjrXpy/naf4y12U6fPmefaJy1FZ7L2gQt0\n4gMfSpIy0zyT0h75bYljrtcMgzTDUdk3OzP8yae1fvq+GobRoZNVSWpsbktSb//XSp/XX1iyW7/8\n34aon8OUYAAAgHbmhSUlPseyMtI81u1dOLav5Qfmz2efE8fIgOQpzGlbL5qb5ZmUjhlQKEmaOLib\n0ryq/zqrAXsfD0V9s3XC2hm4fwlQbjGSunBbuc+xSJCwAgAAtDODe+T5HEszpG2H2qYzzt1w0Gfq\nY2FOhgZ0y417fECyrLz3PEnS85+XyDRNFc0uVtHsYt383HJJ0urSKt3x8peW164urbI87ryHFVuL\n71TYziJY79WPNwVeSx8qElYAAIB2xqoaqWlKPfM9R1TdR4y+O2O4Pv7p9LjHBiRTzy7Zrm2rdZXe\neuRHN23X2Xv1kSvHRXWf9sieoL6zrGEFAACIs+Ullbrmr0s0sFuuFkcxJbe5xa4zfj1PB6s9p9/l\nZqarvrlFb60u8zjerbWlxuyLRuv26cMjfi7QHnm3r7FyyYn9A74erAqws51Ln4Kc0APrIDIT1B6L\nEVYAAIA4u/u/6yR5VvCNRE2DzSdZlazX0T194yRdMXGgfnb+KH3tlOOiei7Qnozo00WSVNcUfH3p\ne+sOBHx968HA/UNtdscobkZ65ytxNnlIdw3vnR/y+WaQKcT+kLACAADE2fZDbR96I/nQ9su316to\ndrFHz9VgxgwoVG5Wun5w7kiPYjRAR+ccNQ1lfWlhbuAJpweONvgcu/EfS1U0u1hVdU2unq4ZnbBV\nlGEYeuuO0y1fy0gzXDM8nJwtgFburgzrOSSsAAAACRTKqI+3f7b2Uj1Q7fvh2Z9IKp4CHYEzeQxl\nDevfvj7JtT37otE+r7+1ap/PsYXbKiRJT3+203XM6KRNpDLTrdNJm93UEK/icM71vl/5y5KwnkHC\nCgAAkEAPF2/UwTAST3ellaFPKSZfRWeV3tqDtSFAy5kbTjlOJXNmaWTfAtex26cP19wfn+Vxnndv\n1q0H2ypxV9Q0qql1FNd7NLGzyPKTsErSvirP9zlbhEWaSFgBAAAS6JVlpXriw60RXeuvHYeVXm7V\nUoHOJLN1PWlDs/8RVn8jg0N6eo4Kjulf6LH/w1dWubanFPVQU+sobnaCChClmrQ0w/XzdtenIFt9\nCjzfgyJtAdQ5f7IAAABxVrx2v98Ko2VHQx8pfWFJSUjn/f66Ca7t+y4Z4/cDOdDRpbdOCfauEvzX\nr5/sc463nMx0j33vafibD7SNsNY22lxrXL17HncmVssP8rLSfY6t3Xs0svtHdBUAAAD82lhWrTte\n/tJVHdibPcTCSyt3H9H9b28Iet70Ub01paiHxz7QWTmrZu/3+mLopOO6u7Yra5v8Xv/QFW09VZ9b\nXOL3vAfe2ahH3tskSTpSF7yFTkdltVa4xTR9lj5E2vOWPqwAAAAx5qyG+fbqMo0f2NXn9cXbD4d0\nn0AfqiWpZM4sSY7Kw4ZhaNdjF0tyVO8EOqs1pVWSpIeLN3kc71vY1it1aC//7VhuPHWIThrcTZf8\naZHH8ZYAazA7829c74JslR/zbLdlt0uHvd6/rEZdQxHyCKthGOmGYawyDOPd1v1rDMPYYBiG3TCM\nyQGum2kYxhbDMLYbhjE7oigBAADaEfdpvN4fmp0OhVB46aevrw7pec4E1TAMklV0el1zPQsgnTqs\nh/K9kiV/U4KdrAo21TT6TvF33mdg99xww+wwMi1+lt4JrBT6zBJv4UwJ/pEk93fc9ZKukvSZvwsM\nw0iX9JSkiySNkXS9YRhjIogTAACg3djiVknUybsIUmVdk+pbW9wcqW2y7LF6rMF6DexTXztZOx+9\nOAaRAh1Pv66eyeMjV47X53ef63HMqlCQO6s1qVa/o84iTZ25yFlV61phZ/9bSa7qye7eXbs/ovuH\nlLAahjFI0ixJzziPmaa5yTTNLUEunSppu2maO03TbJL0qqTLI4oUAACgnRjS03e6YUWN54jDzD8s\n1An3fyBJmvzIx7rnTev1rlZmndhfaUFGiIDOanS/Ao/93Mx0n1HXR9/bHPAe7tOHnZbtqvQ5trO8\nNoIIOxZnb+lgszv+vGBHRPcPdYT1D5LukhRuLeKBkkrd9ve2HgMAAOiwzh/TN6zzW+ymXl+xN6Rz\nJwzyXRMLoM3F4/t77HfJCb9sj1XCurykUrNa790lm1JA3uymqS+8RrLdNVkUZwpF0ITVMIxLJB0y\nTXNlRE8IkWEYtxqGscIwjBXl5eXxfBQAAEBctdhD/2C2ZEdoBZicnr95arjhAJ3O1KFtVbMz02LT\nGCUnM102u13H9y3Q2AGFwS/oZD7ZdFD9uvom+k6nDe8Z0X1D+ds7XdJlhmGUyDGl9xzDMP4V4v33\nSRrstj+o9ZgP0zSfNk1zsmmak3v3phQ7AABov5pbQi8u8o9FO/2+du3kQZKkv904SSP7dNEpQ3uo\ne4StIYDOJNttDap7gaVhvR3T9X979Ylh33Pq0B5qsZtKTzM6dd9VfxqaHV/UXXWS9YTavKzIRqWD\nXmWa5t2S7pYkwzBmSLrTNM2vh3j/5ZJGGoYxVI5E9auSvhZRpAAAAO3EXf9eG/K5gUZY7aY0oGuO\nLhzbTxeO7ReL0IBOYeG2Ctd2hlvCenzfAu0sr42oqu/Nzy2X5JiWv9WisBochvfpYnn86kkDXYXm\nwhHxVwOGYVxpGMZeSdMkFRuGMbf1+ADDMN6TJNM0bZK+L2muHBWGXzdNM3j3awAAgA7mZ+ePsjxe\nG+ADXJPNzkgOECX3AmWPXjled808XtOGRTY9VXKM2B6s9m3b0ll513/bcajG77lWFdSD3j+ck03T\nXGCa5iWt22+apjnINM1s0zT7mqZ5YevxMtM0L3a75j3TNEeZpjncNM1Hwo4QAACgndr2yEWu7aG9\nfSsHe1u687Cemr/dtf+/NWUqOVwXl9iAzqh7fpa+N2NEVP2KM9L5Esnd/DtnSJJyMh0/l/+uslwB\nqsfe3xxRL1Z+2gAAADFkt7d9IHOfithiN3X3RaMDXnvd01/ot3ODdQ0EEMwvLx0Tt3sfrmF01V23\nXMe6+szWRP7Ws4ZZnrf7cJ3mbToU9v1JWAEAAGJoeUlbr0b3UZwB3XJ12/ThKpkzS4VB2mz8ZcEO\ntdjDH4kA4HDz6UPjdu+MGFUd7iiM1h9HXla6JGljWbXfc590m0ESKn7aAAAAMVTbZLM8npuZ7tr+\n69cnBbzHrz/YrP/7eGtM4wI6m1vOGKprJg2K+Po7zh4uSTpndB+P497rMK+felzEz+gI8rMyNHlI\ndz1yxXhJki2Mtl6hoOMtAABADNU3WX9Y65qb6do+bUQvlcyZpaLZxX7vk9M6WvGVkyP/wA10Zvde\nEt204J9fOFo/v9AxjT/Q7+pjV42P6jntXXqaoX9/9zTXfloU64OtMMIKAAAQQ30LsyVJF4zp63E8\nIz28D3G/+cCxlvVKPz0NASTPeSc4fr8vnTAgyZGknlgnrIywAgAAhKnR1qKFWyt0nldSKkldWten\nXuGVaKZ7934IEW1tgOQrmTNL4x+Yq2MNjin/z3xzshptLcrOSA9yZeeTFuF7nd/7xfRuAAAAncDx\n936gW15Yoc+2lvu85iyW5J2gZlKoBWjXbj6tyGOfZNXaxMHdPPZ7F2RHdT/eOQEAACK0+YBvNcyj\n9c2SpHSvaXHpYU4JdirMZUIckAp+cv4ovfjtqdry8Mxkh5LSznUrUrXmlxfov27rWyPBOyAAAECE\nvEdYNpQd1df+vlSS1NziWXwp0hHWwpzM4CcBiDvDMHTmyN7JDiPluc8u6ZqbqcbmlqjuxwgrAABA\nhEb06eKxv3L3Ede2cy3rJz+brieunaDcLN/pg2/dcbpmje8f8BkDuuXGIFIASAyf5RDp0aWcJKwA\nAAARmrf5kMf+/W9vcG3ntSaow3t30VV+WtNMHNxNT91wss4c2St+QQJAAmV7FYrrnp8V1f1IWAEA\nACL0j0W7/L6WlR56QZYnrz/Ztf33b0x2bRdks3oLQPuSnel475vgVXwpUrwLAlG49YUVunziQM06\nMfB0LgBAx1VZ26Qe+Vm68401HsdNmSHfo2tepj76yVnaerBG57u1yvnJ+aNiFicAJMLAbrl65Mpx\nOv8E37ZfkWCEFYjChxsP6o6Xv0x2GACABCvqmefafm/dfknSv1fu9TjncG1TWPcc2bfA5wvQG049\nLsIIASB5bjhliPoU5rj2B3WPfC0+CSsAAECY3IuI3PvWen2+o8LnnIy0yNrYuKPPI4CO4LIJAyK+\nlinBAAAAYepbmKNth2pc+85WNu6mFPWI+P53zTw+4msBINUM6p4X/CQ/SFgBAADCZDeDr0/NyYx8\ndPR7M0ZEfC0ApJpoOtswJRgAACBMNnvoBZUAoLObPqqPa/uicf3CupaEFQAAIETHGpo1b/NBLdtV\nmexQAKDd6Ne1rQDTbdOHh3UtU4KBCJkhTAcDAHQsj3+4Vc9/XhLwnFnj++tgdUNiAgKAdiIvK111\nTS1hVwwmYQUiRL4KAJ1DQ3OLquub1acwR2v2VgU8d8Lgbvrj9SeFtMYVADqTDb+6UKYppYVZQZ0p\nwUCE+DACAJ3Dt55frqmPfiJJSjcCf9CaNb6f0tMMj7Y3AADJMIywk1WJhBWIGOkqAHR8Dc0t+nzH\nYde++zosSfr4p2d57H/r9KEJiQsAOgsSViBC7iOsO8trApwJAGiP3l+3X6Pv+8C1b5qmdpTXepwz\nok+Bx34GI6sAEFO8qwIRcp8RvG7f0eQFAgCIi+cWl3js2+ymenXJcu3/9uoTJUlnjeqdyLAAoFMh\nYQUi5J6wVjfYkhcIACAulpV4tq456zfztXBbhWu/d0G2JOmUoT0SGhcAdCYkrECETPdVrBRgAoAO\nb/9Rz1Y101tHVr9z5jCdMrSH5t85IwlRAUDHRsIKRMjulqO++MXu5AUCAEgKo7VicFZGml67bZqG\n9spPckQA0PGQsAIRMt1GVbcepOgSAHQmt5xBNWAASAQSViBCdnuyIwAAJMu9l4xJdggA0CmQsAIR\nqm9uSXYIAIA4aW7x/61kbmZ6AiMBgM6NhBWIUHVDc7JDAADEyZHaJr+vbXpoZgIjAYDOjYQViNAx\nElYA6LBeXV6a7BAAACJhBSK2s7w22SEAAOLkiY+2JjsEAIBIWIGI/fzfa5MdAgAgDlbtOZLsEAAA\nrUhYAQAA3OyprHNt//d7p2nVfee79im4BACJRcIKxMCEQV2THQIAIEayM9LdttPUPT/LtT+e93sA\nSCgSViBKhTkZ6pqXFfxEAEC70Kcw27XdaHO0t7nypIGSpLOP75OUmACgsyJhBaLUr2uOmmz0ZAWA\njuJX72x0bee0jraOG+gYWR3QLScpMQFAZ5WR7ACA9i4nM11NNv8N5gEA7cua0irX9gn9CyRJN51W\npEHdc3XBmL7JCgsAOiVGWIEord17VF/uqdLi7RXJDgUAEEMf/3S6DMOQJKWnGbpwbD/XPgAgMUhY\ngRi59YUVyQ4BABBDw3vnJzsEAOj0SFiBGGluMZMdAgAghhhNBYDkI2EFYqSphXWsANARDOhKYSUA\nSBUkrECIitfu16o9R1z7OZlpuu2sYUmMCAAQD6cM66njeuQlOwwAgEhYgZDd8fKXuvLPn7v2bS2m\n0tMM3eqWtB5raNa/vtit+iba3ABAe2Wzm8pIYzowAKQCElYgBO5ta0or62Sapmx2U7WNNt1yxlDX\na/M2H9K9b63XnPc3JSNMAEAM7D1SpzQSVgBICSSsQAh+9Ooq1/aZv5mvrQdrJEn/XLJbhbmZrtcW\nbCmXJFXWNSc2QABAzGSmpamqrinZYQAARMIKhGTl7iMe+xU1ja7tnMx01/bRekeiyvfyAND+VDc0\n6z8r92pZSSVrWAEgRWQkOwCgPTh0rNFjv9HmWKPapyDb43h+tuNXqq7JlpjAAAAxc+IDH7q2v9xT\nlcRIAABOJKxABJyjqn+4bqLH8Z75WZLkMU3Y6VhDs7pkZ9DXD0C7c+hYgzaWVcs0pZ0VtfrW6UW8\nlwEAEoKEFQiiaHaxz7EWuylJyszwnFVvszuKMxlek4J3lNfo3Mc/1ZyrxuurU4+LU6QAEB9TH/nE\nY/+hdzeqZM6sJEUTH9UN1B4AgFTEGlYgAs6ENd2riqStxXHce+Bh64FjkqT5Ww7FPzgAQNgqayiy\nBACpiIQViMAfP9kmSUr3ykxfXV4qSfr3yr0exw9WN0iS5m44mIDoACB2TNNMdghJMbpfQbJDAACI\nhBWIiLMYR6hLuFaVUrwDQPu0bFdlskNICO8ZM29+7/QkRQIAcEfCCkTBOfBQ1DNw+4OG5pYERAMA\nsffIe5uCnvP26n0qml1suea/vdh26JjHfm5Wup8zAQCJRMIKRMHemrE+dcPJAc87Y0SvRIQDADFX\n4dXWS5KG98732P/Rq6sTFU7cPLe4JNkhAAAskLACYRjiNZLaWntJeVmBC27bO+cSMAAdwC1nDpMk\nzf3xWSqZM0tj+hdqaK8uSY4q9qrqqBIMAKmIhBUIw+7DdR77LSFmosf1cCS6hTl0kgLQvnRpfd/K\nz3ZMkc3MSFNTiz2ZIcXF9bQcA4CURMIKhMiq56Cz72pmum/1pV0VtTpY3aDXlu+RKUdiW91gi2+Q\nADqkuiabnlm4U/YkTNfwbuOVnZ6mPYdrEx5HPP3o1VX6xZvrkh0GAMACwz1AGLpkZ6imsS3pHDug\nq3miGZIAACAASURBVCSpT0GOz7k/eW21Gm12bdpfrTlXjU9YjAA6nkeKN+mlpXs0pGe+zh/TN6HP\ntnklrBU1jSrMzUxoDPHUaGvR26vLPI79+iu8ZwNAqmCEFQji2smD1L+rIyEdO6DQ47WurR/asjJ8\nf5VWl1apvLVYyeYDbdUnj9azTgrorBqaW/Tp1vKwr3tp6R5JUos9cVNxbS12/fCVVdq8v1qSlJHm\neJ/LTE/T2r1HExZHvNV4zXyZPqq3rpvC9GAASBUkrEAQdlNyTvj95mlFYV1b1+T4IPT85yWuYxN+\n9WFsAgPQ7vzqnQ365rPLtKk1CQxFo62tLVZTS+KmBP9j0S79b02ZK1nOa23zsuXgsUCXtTtH6po8\n9m85c2iSIgEAWGFKMBCEaUqG4UhZLx7fP6xr65rovwp0dlsPHpPdNDW6X6F2ljvWfm4oq9YJ/QuD\nXOngXr02P469QY/WN6vJZlfvgmxJ0t4j9R6v52R6Pruqrkl20zES257Z3NYFfz77HA3olpvEaAAA\n3hhhBYIwTVOGb00lH726ZMU/GADtzgW//0wz/7BQkrR0V6Uk6c431oR8/SmPfuLafnL+9tgG5+a0\nxz7RlEc+du0P7ZUf4Gzp5ueX6+SHPtJUt/gk6bMIpjwnU7OtLWHNzYzfFwIAgMiQsAJBmJLSLDLW\nzQ/N9Ni/etLgBEUEoL1ostkttyO1ak+VahvjU2281m1GiN1uWlY/l6QfnTvSFYuV9WWpsb61rskm\n0ww+hbrZbV2w1Xs9ACC5SFiBIOymqTSLzzDe0+Oy/Hy4A9B5jbr3fcvtaIz95VyVVtYFPzEK97y1\nTve9vcHytf/7ZFvAa3/zwZZ4hBSWjWXVGnP/XJ39uwVBz/3mP5a5tjMzeB8HgFTDGlYgCLvbGlZJ\nWnjX2X7PC8U1kwbFIiwA7djofgVRXb/14DEN7pEXo2g8PTV/u15ZVhqXeyfK4u0VkqSSw8ET+2Nu\nI9Z5WXwsAoBUwwgrUsr8zYdUNLtYLy4pSXYoLt5rWAf3yLP8oNgtL7S+hImr8Qkgmaq8qs+623zg\nmG5/cWXE935v3YGIr/X25wXbVTS72LX/27nJHyEN1/p9R3XaY5+4Rp7dKyuH6oIE97cFAISGhBUp\n5ebnl0uS36loyWCaoa1ruum0Ij12Fc3mATi491+28sGG0JLOSUO6+xz7z5d7I4rJSihTeB+/ZoJr\n+8v7zg94br/CnKhjCtdLS3er7GiDq8ftmAGhVWCWpFvOcLSxefzaCUHOBAAkAwkrEITdNBXKqqaM\n9DRdP/U4DeoeuCVCCDVAAHQALaGuEwigaHaxVu4+or6F2SFfs/dInW545gtV1DQGPff15aFN/f2K\n21KGHvnWFdFnHN9bknSguiGke8aScwpzdobjY81fP93pem30fe97FF9qaG7RtX9doo1ljl64Da2j\nsd51CQAAqYGEFQgi1BFWpzNH9gr4+oYUqaAJIL5sMUhYndx7sQbz7KISLd5+WO+v2x/03Lv+szaa\nsDy4j8ImS25rn9plre2DJKmh2e7RE3t5SaWWlVTqwXcdM3kKcxzLOTLT+UgEAKmId2cgiA82HNCW\ng4Gn9nkKnNwGmyYIoGOorg+eZN74j6W646Uvg543sFvgmRvu5rZONd4U5L3mV+/EdulFzy6hjwLH\ny0tf7LE87j6FOr217PsXOx1JbZPNrrwsRlcBIFWRsCIlff3U45IdQsS+f84Iv/0LAXQefwzS/kWS\nFm6rUHEII6E7K2pDfu6+qnpJ0stLrZM3p+cWlwS91wVj+uqHrX1Xw1HfFH7Ro1hYsvOw5fEH/teW\nnKe7zZgxTVPNLXZGVwEghfEOjZQ0vHeXuNx3Y1m1rv3bEtljOFXP28BuuVrzywvidn8A7cO2QzUh\nn2um4OL2+y8Zo6e/MVk/PX9U2NdGUqU3kPqmFr20dLfPz6nR1qJL/7Qo6PXuCWm6W2PtDWXVKj1S\nT8IKACmMd2ikJFtLfD68XfzHhVq2q1Jf7jkSl/s70csPQNfc0FpdSdI7a4OPsiZarVt/Um9XnjRQ\nUlvyN2FwN49/N9nsMY3lt3O36J431+vjTYc8jl//9Bdaty94XYArJg50bbsnrJf8aZHyszNUWRu8\nQBUAIDlIWJGSHnlvU1zvfyzABzFvEwZ11fRRveMYDYCOpLnFrqU7D2tsa2uV26YPC3rNviP1Psec\nVWytnDO6T0ixRDObpF9X/+1pfn/dRJXMmaUdj16skjmz9PYdp0uSjtQ6es/+d9W+iJ9rZf9Rx89n\nwZZDqmuyuY59uafK47xRfa1n5/R1+7OUHPacXv3OmjIVhvHlAgAgsUhYkbJeWRZ4/VU0Hi0OPSFu\nMU1lpMV2TWqgkQsA7dtj723WdU9/oa0Hj2nCoK76xrQin3OyMjz/99u7wLdg0cV/XOix716s3F8F\nYu8ps39esD3EqH3VRPA+VX7MMVK5PYzp0KFw/tlfWrpH97y5XpJ0+4srfc7betD6ue7riX/y2hqf\n18OpwgwASCwSVqSsUKZ5RSqctWW2FlNpESSszlHZf3xzsrY9cpG2PDzT9VpjjKfLAUgNuypq9ezi\nXZKkipomZWWkeXzhtfr+87Xz0Yt13eTBHtdZJazePvjRWa5tW4v1e8jLXl/0zdt8yPI8d/PvnOHa\n/ugnZ+mm04okRTat1/nncL+2srYp6iJMuZltyyw27XeMPK/Za/3/CH/rgRduK09aMSgAQORIWJGy\nglW4TBS7aXpUlQzVqcN6SpIGdc9TZnqasjPa2iZ8trU8ZvEBSB1n/26Bx36jza6czLbf/bysDKWl\nGTrpuG6e5zUHT6Tck1p/I6wLt1Z47HtPmXVyH3Hsntc2HbZrXqamFPWQJI3oE37xuxnHO76oc4/u\n5Ic+0qVPBi+MFIj7d4aVrdOO/fnBK6ssj9/4j2U649fzoooDAJB4JKxICbYWuxqaW8LqNZgojTa7\nR5GOUN121jB9+vMZOr5fgc9r6+M4egwgsZpsdr+jkT3zszyKLzmnAjuLFjk98dHWoM/pkZ+lv9xw\nsop65vmdpZERYkut/60pc213y8vS/Dtn6PXbpqlPQY4uGtdPH/z4zJDXybr7UWsLnEleCXm0U4Td\n34P7FGar2c8IsyS9G6CA1eEgyS4AIPVQyhQpYcQ97yc7BEullXXafdjxz1NhXpuWZmhIz3zL15aV\nVEYfHICkq6pr0sQHP/L7evf8LMvjhtesjVCXKVw0vr+++9KX0uE6Pbtol751xlDXa0Wzi0O6h+Sb\nQA7tla+hvRzvV2lphkb3Kwz5Xu6cCfkD72zUTacPDXJ26F5dXuraXr+vWiNT9P8ZAIDYY4QVCGDL\ngWNxuW9ZVUNc7gsgsfYfDfy7HGrBtu9OHx7w9RX3nudzLJqCSvHiXUwq2V75zql+X7vzgrb+svTO\nBoDUlVr/ZwG8xLNARtHsYtntpiY//LFeX1Fqec7uyrqYPvMf35wsSXr0ynExvS+AxLr8qcUqml2s\nZxftCnjegBCXOWSm+//fcdfcTPXq4luUqaKmbXrrj161XreZaFluf46i2cX698q9Ud/zpaW7I752\n2vCefl/7/jkjVTJnlkrmzAqrZy4AILFIWJF0gfoElh6JXcJoVTmyqcWuippG3dvaJsFb8doyy+OR\n6lPg6AWYFkERJwCpYe+ROq0pdRQzeiNIQtYl27Hy5o3bp+n126b5Pa+pxf+Xcx//dHrQmN5eHfp7\nVTS9WYPxnup85xu+LWTCdY+f92dvf//G5JDv+euvjI80HABAgpGwIumOBej1d6zB8dpLS3frmYU7\no3rO0XrfPnvOY00tdtntpn76+mqtLm2rqlly2JEwF+bEZrm3syDK4h0VQc4EkGrmbz6kotnFOuPX\n80O+Ji/L8d4xpaiHpg7t4fVaW/XgneW1PtfmZKbp1rOGhdTyJpij9c265Z/LVVHTqNqmtvfcWNw7\nVPFKlN+4fZqe/NpJOmNEr5CvmTrU/8grACC1kLAi6RZv95+8vdE6VfeeN9fr4eJNUT1n6S7fQkev\nuxXyqKxr0n+/3KdvP7+87VhrRcmnbjg5qmc7OQcfnltcEpP7AUicm93eG0IVqPrvf757mquqbrc8\n3+JMzS2mxxRbp5O9KvBaef7mKR77ry7bo483HdLTn+3UKrdWN3+O0XtbKJrt8ek/PaWohy45cYBy\nMj1/Vj88Z4Tfa1JtrS0AwD/esZF0NQ2eI6zjB3Z1bVfUNHpM5W2J4hv64b19ewq6t4ZwTmQ7XNsk\n0zQ9RgN65sdmFCIjjV85oD35zgsrVDS7WFf9eXHI18wc28+1XRtgBskJ/Qv1k/NHqX/XHNm82rQ0\n2exqsZuqrPNtw3JC/7YKvqZp6qONB33OGdPfs8qvs29rRU2jfv3BZtdxZ8/VRPDXiidcy+9pK0D1\n0/PbCicZhqFbzxrm2h8/yH9iH2oxLABA8vHpGUn34heeBTVu/v/s3XecFOX9B/DP7O71ynHHcUe7\no1fpTUBRQMBT0WhsscSYqDFqmiIa1ERjcjGJiS2WqNGfvRBbDrEgICjSO0g/ygHXe90yvz92d3Zm\nd2Z3dm/r3ef9evlyyjMzD8fdsd95nuf7nVEgbX+5rwJHqlxT5e5dtjOoz15zoFLalq8r3Xq8Fp/I\n1q8GK87khySi2OIMBrfKRiV9ee76iUiKs0/39bZu1clkFDxexq07ZP/d9OaG4x7t5UHZzpP1+Nn/\nbfZo09MtSdOeU/baz//dWoY9pxp89qkzHl40SvX4p7u066PqdcnYfGTJSgVdMbGv4rz8RaMz4dLz\n10/0uE+WRrkhIiKKPqzDShFXdFYedpXVS/uDeylHQuW5kjqzBkptdPZAuatsjU32IFFUZigWEJxA\nUz4NrcNi47Q0oi5q3yMLdLc1GQwwu/1+OlnbqtleXt950TPqI79Gg4A7zx+Mp76yl75pbPMc6XX/\nXRssN0wvQEVDO55epSy709LJrO+PXzkWP5hgD1BLi4tU2wyXjSw7E17NH9UbpcVFijq13rIyExFR\ndOFvbIq4fj2SFfsGQcA984dJ+wmyoG7B6N4IlDNgPX94L+mYfIqaVRawCoLyw1ywkvrK3+p/siO4\nGYiJKDImDeghbf/47AK/ry+rbcW+08pRz+JP7dN2x/bzvV5VS5NjOnKb2YppAz2TDF18Vn7A9/bF\nPVgFoFqaxx9TVf4M7kKZAZmIiCKDAStFnMUtEYfRIOCaKf1V2+amJwb8HGfAOio/XfX81mO1iv1U\nWWbgYJWhSYxzZQWtamoPyj2JKDIWjbMHfENy7S+3BvRMxu8vUZ8O602H1YZDFU3S/u6yemk08rJx\n/gWVe/4wXxp9LMy2j8Q2tVtUsxAPz0vzu6+d8dbG42hs88zW7kthdgouHpuPPjpq2manaU/1fdGP\nsjdERBQ9GLBSxDW3K6eJVTd1KEZVX1p3VNpe+qG+enxqnCOo7iO6TvK1slYbYLG63tRnpwZ/vRNr\nsRLFrszkOKn26Vsb7dnGLxnbuRFLZ4K5i55aJx2zaIwYOoNRd87SWYB92QEA1LWYsWyrZ73YUE0J\nBoCJslFnp28PV+OSp/Unr3IyW22I07n+/1i1du3ucAfoREQUHAxYKeIeLdmr2K9rVWbFrJSNRMrX\nuvrLOcKam5GIqyb18zgvX+NV29KBk7WuDz5qJSc6i/EqUXg0t1sU69WDYevSeYr9fQ8vwK/nDtVo\nrU91cwfq3LICZyTFqbb9+I4ZHsf2PbwACSbXLI7eGfYZKVrZ1dUypwfL+7epJ5s6WuU50utLc7sF\nRp0Bq7MUmZq+Gi8riYgoujHpEkVcs1sijt7piYoPJxUNbUF5jnMqWofFhsmFWXhn8wnF+Z6y9aW3\nvrYlKM/0hiOsROEx6qHPANiny6YkdP6fPUEADG4BVFK8UaO1fpP++KXHMXmCJbmUeM8/h3sfVu6r\nAABc8vQ6j7ahJgTx91ttixmtZn0Jm/SMGg/MUf+aEhFRdOIIK0WN126egocXjcKkgizFWs/yhuCs\n9Yx3ZIVMTTDh8gl9PM737eF7fVQwscINUXg1eamJqkUt7nIeGt3Hvh7+9ZundqJX3k0pVK+TajAI\neOTS0V6v3Xrcvi4/WPVPA3WrrAxPIJLjjVKZIF/kSfXUrF18Hj76hefoNBERRS8GrBQ1Buak4obp\nBR7Hj9dor0lasfs0CpaU4Ey971FY51qweJOg+vb/tte3ql4X7kCWiEKjw2LDM6sOoWBJieY0WXd5\nKonenJfOGJQNQHvabqhdP22A1/Nq60jDLTXBhAlu/bjmhe/8vk9msr6vsXPkWevP3i8rGWmJkfn7\nIiKiwDBgpYj7oaPwu54MkO7edCQ7+f5Mg4+WrnVcRoN/3/a3dHJ0QIuV1ReIwqrdYsNfP9sPwF7q\nRQ/3+qhyd84ZgqeuGS+NtAbqi1+f06nrtRT/4CzNcy/dGPqMuR/cfjbevXU6RuYpvz7rj1T7dR+L\nTfSYgq3FYBDw0o2T8MTV4/x6BhERRS8GrBRxNc0dSE8MbF3Z3lP2JEx1LdqlEk7Xt6JgSQluemUT\nAMDk51xcvVPR/LXpaE1I7ktE6ioaXTMxGtzKq1z81Dq857auvaXDgspG7SUJqQkmXDw2v9PrNYfk\nhiZ7bbxJ/Z/466b1x5wRuSF5ptz4/j0wMj9dNdisb9Vf3sZmE2H042s8Z0QuEywREXUhugNWQRCM\ngiBsEwThf479LEEQvhAE4aDj/6rzbwRBKBUEYZcgCNsFQdgcrI5T19EzNR4Nbf6vLQOAqiZ7Rsiv\nD1ZqtvlkxynFvr/Jji4b77netTPSHElfVuw5E9T7EpF38lJV7i+idpXV4573dyqOqdUuBYC5YQj2\nAGDV3bN1t33nlmm62963cEQAvQlcn8wkjOuXqTi2/rD+UVaLTfT7RSMREXUd/oyw/hLAPtn+EgAr\nRVEcAmClY1/LeaIojhNFkVW7yYPFJnZ6najWh5kTNS340/LvlW2N/n3wMRmDOxHhvgvD+2GRiOzk\nmWbl9U2d9U/dJbiNUJYWF6G0uAgvhmE67bYH5mnWWlUzdWBP9eMqSZuCkSnZXx+6JToyW+2JoMrq\n7DNgNpWqzzixOf6e9E4JJiKirkfXJ3FBEPoCKALwouzwIgCvOrZfBXBpcLtG3YXFqv72XM9AaEFP\n+7Qvreltd729zfO+/nUv6GYPy4lwD4i6p1e+KZW25aOtWstU5UGSr4y8waY3ydBHv5iBn88epHle\nXiJsXL9M3DSjoLNdC9hDF4+UtjscmYuLP7W/UPzhc+tVr7E6XiZwhJWIqPvSO3T0TwCLAchz4+eK\nonjasX0GgNYcKRHAl4IgbBEE4RatBwiCcIsgCJsFQdhcWak9vZO6HqtNVB3FTNaxdrSPY2RWrSYh\nAGw7XudxzL3Ew13nD9bTzaCRT0V8d9MJ7Dzp2UciCr402Vp5i831e0CeMdi5tnXF7jPYesxeFuap\na8b7zMjbWaXFRfjg9rMBAFMKsnSvix3bLxP3Lhiuef5AeaO0fcP0AXjo4lGd62gn/PjsAml7z6kG\nPLPqED7ddVo69vjn+1GwpAQFS0qkY0N+9ykAwMwsdURE3ZbPgFUQhIsAVIiiuEWrjWifT6X1r8lM\nURTHAVgI4BeCIKimQxRF8QVRFCeJojgpJ4cjUN2J2WpTfXt+70LtD2HuTtW36m7rPvtvfP/wln5I\nl5XAWLxsJ276z6awPp+oO5EHo5/vLZe25QFQabVrrepZv/8cAHDb61ukNa3hGt0b3jsd4/tnYvGC\nYUG7p3OdPxC58jtO8iD85W+O4q+f7VdMzX7yq0Oa1z6x8mBI+0ZERNFLzwjrDACXCIJQCuBtAOcL\ngvA6gHJBEPIAwPH/CrWLRVEsc/y/AsAHAKYEod/UhZTVtSqmrTlN01iTJffNIXvijsXv78THO05p\nrkWTE93erYgQcc2Ufjp723nuf9bq5g6NlkSkl8Vqw6zHvsKxamWipMVuiZScXl53VNpubvee9C1c\n6yeT4o344PYZmFTgue40GIK9Hj8QH98xw3cjAN8dqdZVX5uIiLo+n/96iaJ4nyiKfUVRLABwNYCv\nRFG8DsDHAG50NLsRwEfu1wqCkCIIQppzG8AFAHYHqe/URcSbDKhq8iwd0dKhr06i011vbcP2E76n\n1/ZzK3fQbrahMcAsxUQUHR74aDdO1LTi3L+uVhxftvWkavu3Nh6XtlN9JCG69TXNCUYxZUTv0JTP\n8YdJZx3sq1/4DvP+sUbaLzorL1RdIiKiKNeZ163FAOYJgnAQwFzHPgRByBcEYbmjTS6AdYIg7ACw\nEUCJKIorOtNhik77TjfoG90URTxashcnalpkx4ChKnUItQY1th23rytrt3gGtHpKxfRIiVfsJ8YZ\nkeV2jIhiy55TDdL29hN1sNpElDdoj9DJp6K6rxdtM/v3sixW9EpPjHQX/MrS7nyReP7wXnjm2gmh\n6hIREUU5vwJWURRXi6J4kWO7WhTFOaIoDhFFca4oijWO46dEUbzQsX1EFMWxjv9GiaL4aPD/CBRp\nn+05g4VPrMVH20/5bPvlvgr8e+1RzHpslXTMbLV5lI8AgN4Z6h+uLvvXtzhR04JnVx/2OPf8miO6\n+720yF5eZlBOquZbf77VJ4oNibJkZpc+8w3e23wCU/+0UrO9fF3q8Rp904ip8wJZDxwfBVOZiYgo\ncvivAHXaoYomAMB+WTZKLd+fblDs7z/TiIrGdsSrBKy90hKx/K5ZqvepamrHqu9Vl03rdvPMQmx9\nYB7691ROEb5kbL60/ZfLz+rUM4goPJLjlVnF1TKEy80Z0Uuq8Wlxy0D7zaEqxb7z5VYscmYejhZ6\npwTL6UyYTEREXRQDVuq0dQftH+42HlUv/C4nL1j/2vpSzP/n16hsbNcsWTAyP131+Io9Z7DjZL3q\nuSOVTT77AdinAapNBb5qsisBE9/sE8WG1fuV5dDe2XzCa/vP9pTjL5/Za4A+u0Y5W8M9EVr/LOVL\nrVgS7izovvgzJdjp092+l3oQEVHXxU/j1GkHHSOs3taLORVkuz74vfJtqbStNsLq9O8bJuGfV41T\nHHMGyd764w/nLLULx/TGjMHZuvpFRProWd8eCc4lBEermr22u2BU73B0J2TWLj4PG++fE+luAPBd\nWuer354bpp4QEVGs4Kdx6rQZg+3lZzosNp9tf/LKZmn7cKXrQ2LJztNqzQEA80bm4tLxfRTH5AlW\n3G11JGXyR35mEgDggpH2D6aDe6X6fQ8i8tTQZkbhfctRsKQk0l3RdMs5AyPdhZDql5UcFQmXAM+p\n2wAwKCcFAHDD9AHomZIQ7i4REVGU857Ln0gHZ8KkikbP0jTexJsMuoJcp0kDemDzMd/B6FsbjuO+\nhfY1Z4lxBrSZbVhaNALj+2ciPVH97f61U/sjKd4oJVl6/vqJHvUcich/p+paw/KcC8f0xvJdrqmj\nUwqzpGUKS4tG4I8l+1Svs9lETv0PI/eMzACw7OdnY/X+SiwY3Ru2KB2NJyKiyGHASp0mILCMGP5O\nE3zjZ1MxbKnvqkgNjlIIFY1taDPbMLUwCz+d5X0EJTHOiGum9Jf2B+WkYlBO+EZZG9vMSNMIpp2c\nI1RbH5jHMjxEbhJNypE7Z7B688xC/HTWQM2AdeD9y1WPU3iUFhcBgDSLxmLV/xKTiIi6B75Wpk6T\n1zP0JS3R9Y7E32yRCSbPqWTevPJNKQBgg45kUOG24lfK7Mdf7ivXfe3bm44HuztEIWMLU/xhsYmq\nJVOa2y3h6QAFhUlltPu+hcMj0BMiIooWDFgpYPUtZiz9cBea2s0e5z7ecQoFS0o8RlFvO3eQtN1q\ntkrbs4ZkI9je2eQ9S2gkDe+tzH7cZtb/qX6NWzZUomh1qKIJFz65NizP+njHKdWXZ+5lqwCo1n2m\n8Ont53raAT1TQtQTIiKKBfxXmwL2jy8P4PXvjmOro96hM/kSANz11jYAysRKAGDVGI3tlabvA8wr\nN032OBanUSbhiol9dd0zGmwu1Z8oKhpHjInUzH18TaS7IK1P/fHZBdKx566fiL49kjzaPrxoVLi6\n1a29+bOpAICSu2aqnn/qmvGKlwqzh+WEpV9ERBSdGLBSwNot9hHSqiZ7siWzxTMYdc+voTV92Hkv\nX2YP6+VxbGhumsexsrpW9IihdZ6bShmEEgWqzEtiJ2dpqoWjXaVpzhvWC+vuPd+j7Q3TC4LeN/I0\nMCcVpcVFGJWfoXr+4rH52P/HhSgtLkJpcRES4/xbDkJERF0LA1YKWG2zfSpwpqOu3kaVoMs9r5JV\nY0GbfKqwv9RGbWcUfxXw/SLheE1LpLtAFLMWPf2N5jmz1f77YWR+umYbwJ5VmIiIiKIPA1YKmHPk\norbFcw2r05sblAmCymo9R0JKi4swuo/6m3ZfxvbLRH2r+vO1ph9Hi9LiIvxm3tBId4MoIMeqm7Hj\nRJ3XNn+6bIxiX+9MCr3e33ISH20vk2Z5ZKXES1lnnQY6anymJcZJI3Zq3r11OgDgwYtGBrWPRERE\n1DkMWClgpTrqlL78zVFUyuqzfrj9VFD7cPFZeThd36Z6zp8ar5Hi/DANAJs5LZhiyI0vb8SiZ7RH\nNgHPGRUPfLg7qH24+70d+OXb26X91g7/AuLzHGsj+2d5JmYiIiKi6MCAlQJ2uKJJsd+3RxLaLVaP\nzMD+foj0x4zB2tmF2xyjOUaVUhfRIjs1Qdo+XNnkpSVRdCmttk9j//qAetbq+hYz1hyoUhzbcaI+\nJH0pzLa/+MnLsCdv+/SXrrJRSV7WP/7npikoLS7C14vPk47Z/KwPTURERKHFgJUC0mGxodktED1Z\n24p73tuJe5ftVBzXmrIbqPwMV0Zhg3tWJxnnucscBemjkTxT6b3LdkWwJ0SBueHljVi+67TH8bEP\nf+5RX3h8/8yQ9CHdsY7e+bPeI9mVcM3fEirfHKry3YiIiIjChgErBaRNYy3aqu8r8O7mk4pjPD3r\nHQAAIABJREFUzphSFEVcMDK3089OkI2YZDg+qLq7aUYBeiTbz90zf1innxkqfXvon4ooD9SJIqG1\nw4qGNjNsbuvDd5z0vpb1f3fay5cEulbdJ1FEbnoCfj7bnrytd0YiXrlpMj76xQwUZPtXw3NXWYNi\nf14QfmcRERFR4EyR7gDFppfWHlU93thu8TjmLGXzxMqD+HxvOUwGQbO8jR5Hq1xrZ1MTld/CiXEG\ntJlt+M83pdIxraA21sTL6hJWN7Wjp2w6MVE4jHhwherxHLfvxcdWfK/YT02w/5wu/XA3slPjsWB0\nXlD7teOkfaqxySiv3elZAkuPNrPyZZx7cE5EREThxRFWCsgTKw/qbutMvPKGI2OwPFh95toJnepH\naoIJ+x5eIO1/fIdnIfquUsNP/rm5sqlduyFRmI1xGzn91+rDiv3kBNfP4G2vbw1LnwLlnsnYZIze\nNfBERETdAQNWCsi0gfprFl7+7HoULClRZAt2unBM7073JSne9WF4aG5ap+8XreTJYNYdrMLGozUo\nWFKC8gb1LMlEwXS8WrtW8BpH4qX9ZxpRsKTE43yCSfnS6EB5Y3A7F0SDeyl/h+RlJGm0JCIionBg\nwEoBefCiUZ26/tJx+Xj5x5MgeEmapKWrlqCYNUQ74zEAyJOXvvD1Ebz6bSkAYMNRlsOh0Nl+og7T\n/7wSr284ptnGOaL6wbYy1fMJJuU/Ncu2nlRt5w/3bOTB8vYt0wAAz18/EX+6bAzuXTA8JM8hIiIi\nfRiwUkD+/Om+Tl3/z6vH4/zhgSUzmVxgH919/eapnepDtHjuuokAgLUHvWcnLatrlbZFAHtO2dft\ntZtDVzaI6KGP9+B0fRteW68dsDodrfIszfTY5WfB5FZa6vk1R1CwpAQVAc4OaGq34Mrn1wd0rS8Z\nSXEoLS7C/FG9ce3U/ooZHERERBR+DFgpIN6CqzRHgpVx/UJTwuLu+UNx4/QBmFKof1pyNFswWt+0\n6KwUV6mOysZ2qQ5maXWz1iVEnZbuSGzW6uXFyE9mFAIAPtujLGPz9LXjceXkfpq1kK94LrCg88u9\n5dhUWqs4trRoRED3IiIioujGgJWCrrHdggn9M3HtlP4huX9eRhL+sGi0ImtuV9fSYUFNc4fqubF9\nQ/NigAgATtcHvkbaWRJGa+r/oBz/Ss44bSpVToPPTI7DT2cNDOheREREFN26zyd+Cqvq5g5cNFa9\ndMXcEYGVm+jOPtlxSvNcnLH7/BibrTb8a/Uhj9IjFDrOslBTCrRnNCTG2b8Hr5jYV3E8zuD9e/PO\nOUMC6pMz47jTv37UuWzjREREFL26zyddCppajZE+ucLsFCTHm/DVb8/1OPfijZOD3qfS4iKUFhcF\n/b7hMmd4L6R4WSvX3K4doD3w0W7Nc9+facCPXvwOH21XT4YTK7Ycq8Wuk/V4ad1RPLZiP/616lCk\nu9RtbDtun3qbnuRZtvvzX5+D5HgjOiz20lVDeqUqzhtkU4HvPH+wx4jqgTPa2YJFUcQVz36L745U\ne+3fc9dNwNmDvCcsIyIiotjFgJX89vgXBzyO3Th9gGLfOerXOyMxLH1SM7UwCxedpT7KG21qWjpg\n8JIxec+pBs1zJ2tbNc8t+OdafHOoGr98e3tMl7+5/NlvcfHT6/Di2iMAgHc2n4hwj7oPZ/3flg7P\nlyaiaP9ZN1vtAau3pN+/vWAYfnHeYMWxJf/dpdl+z6kGbD5Wi6tf+M5r/9rMNq/niYiIKLYxYCW/\n7ZfVULx6cj8AwLDe6Yo2zvWlyfHKUZltD8wLce9co63v3DodT18bG1MFz+qToRiNenPDcRypdGVc\ndY5yOc0ZrpxWfagieutaBlNVk310v7yhPWRlTUjdt4ftI50v/3iSdMxkFNBuseLV9ccgiiKW7zrj\n9R67y7RfvLjb6+UljVwAlbGIiIgohjBgJb9tlNX9nDvCnlRlbL8MTBrQQzpe32JWvdZo5KdLNUaD\nAVbHUNbne87g/g924fy/r5HOH6lSZgIela98QTD38a9D38koML6/K8HU/3aejmBPuq8BPV3TerNT\nEqQRzv/tPI3tJ+q8XrtwjL6M2ACweNlOXe1W7qvQfU8iIiKKPQxYqVPmjszFgT8uxKj8DPz1h2Ol\n4/mZrqnAT1w9Ttr2lYSluzIa7LUlRVHE8ZoWzXZ//sEYCALQbvV/GqTFFpsjkvKR1LMH9ZS23b9O\ntc0d2Hi0hgmZQqxXWgK2PjAPu35/ATKS46Tj244rg9VDjy70uHaySuIm59/vqbpWfHuoCs3tFq/P\nL3V7eeNMCkVERERdk2cWDSI/Oaf/ykstvrv5JB67wh7AbpCNyGrVY+zujjlqqr649ij+881RzXaj\n8zNgEASYLf4Hn2ZLbK71M1tdf1b5LOCD5cpp0NOLV0qjfbGcgCvamQwGpCV6Jgh72e371qQze/U/\nvjiA31wwDGcXfyUd8/b3N/tvqxX7P5jQR9dziIiIKDZxuIv8tmCU+rQ+rSWFb8pKUJgYsKr6fG85\nAODZNYdxSqXu5U9nFgIAxvTNgE0U0WH1HEW0+Bh17QhgVDYaWGUjw/JvsaNuI21MvhMeSV6yWTut\nu/c83fdbttV7Buvc9ATNcyt/ey7G9++heZ6IiIhiHwNW8tuKPeqJVfR8kDUwYPWqRqNk0IvrXKNX\nogi8/t1xjzbyESo1F/zja7y0Tnv0Nlp9sa9c2n7lm1Jpe8fJes1rCpaUoGBJSSi71aXM/8fXmPkX\n798//ujbI1l327I6zyzX+067Ei55m8k+KCdV+yQRERF1CQxYKWhy013rVjfeP0fafvzKsWrNKcgq\nGtt9tvm/9aUh70ewyYPsVq5PDYn95Y2a5ZHyZKWpvllyvsf5566b6Neznv3RBPxoan/FMZtbVLr5\nmCsrttbMgbwIlswiIiKi8OEaVgqYtw+MvWTBq7O0jXutVgo/51rZWLLDR+bZ+hYzxj78eZh60731\nyUzyOHa+W4klXxaOycPCMXl4Q7ZUwD0hWIss8VJtixnN7RaMeugzRZthvdP8ei4RERHFJo6wUsAa\n2zyzeb5+81Qs+/nZimMXjMzF0qIRuHfh8HB1Lea8fvNUAMD10xjUezN7WI607Uy28/EO72sgqXOs\nPrJLO5Ou+WvV3bOlbYtNOYpqdhtVlU8Rdnr+ev9GdomIiCg2MWClgPVSSYYyc0g2Jg5QJkExGAT8\ndNZAaaSVPE0ptJf7eO27Y4rj7lMlAeUoV49kZUmP1o6uPWVWXsLkv45kPS+sPRKp7nQ5n6usT7dp\nZVPrpMLsFJwzNAe90hIw8kHl6Kn7I694br3H9Qkm32vmiYiIKPYxYKWAvXjDpEh3ocvQyp5stnmu\n35OXBuqXpUxuU7LrtLStFuzGuhW7PQOqEzXqay/Jf7e8tsXjmK8RVgAY0ydD2vZninB9S4dqHVW9\nJXGIiIio6+OnAvJbQc9kXDI2HwOZoTNotLInD1u6Ah9us48kFmanAFAGrO6B7vYTrmQ1asEuAIgh\nGjELh3a3WrIlO09rtHSx2UQ8ufIgajUyMJN3tS1mn20+uXOmtD0kV//vhQE9Uzym/wLAxqPVAIBb\nzxmo+15ERETUNTFgJb91WGwBr1sj//3qne0AXHVH5TGq++jX5IIsafubQ1Wq99vmI4lRLPnFm1t9\ntll7qAqPf3EAv/9kTxh61HXdPnuQrnY/mVGo+57xJgM6LJ4B66r9lQCA5bvVX0i8+pMpup9BRERE\nsY1RB/ntVH0bA9YIOuIIXNWYDK6/lzaz+ghrrFXCneS2Jtpf1U32cj8tXXx9b6DcR9xFUcS8x9dg\n1f4K6dxdc4Zg8QLvSdNKi4tQWlykKG/lS7zJgFP1bZrnUxM8pwsDwLlDc1SPExERUdfDqIP8lmAy\ncHplmKXEG/HTmfaRK3l8kZehLDPy7JpD0vaX+8pV76W2ZjCaWTs5hfk/35QCAFZqfD26u8OVyhcg\nqw9U4mBFE276zyZpBD9OY8p6Z7ln/3Vmfnb61dwhIXkuERERxQ4GrBSQ/m7Jfii0mjusMBqVQcOt\n5w7E8DxlLcoz9e2ybfWRq7gYS2hT09yhWv9Tza/nDlXs//Wz77GrrB4A0AVzUAVFbYvy5dOpOlcS\nq52Or537916w9EiOV+y7TyeeP6p3SJ5LREREsSO2PrlSVDBbuYY11HqleZYMciZfcnp+zRGPaa4d\nFtf+1MKeqvcOVZmSULGJIsrq9GUC/uawct3uM6sOS9sDc1KC2q+uosmtnnKdLMnSD/71LQDtLNad\n9dX3FYr9vAz904mJiIioe2DUQX6xWG2wibE3ShdrXrt5qsex8oZ2j2MWqzL4NMv2JxXY136+c8s0\nRRs9ZUqiSXlDO5Lj9dXcLKvVDmzH9csMVpe6lOYOZcCqlkVavjY6VMb1y0TPVM8XNZuXzg35s4mI\niCh6Meogv7SY7SN4apk9KXj0BpUWt9I18nqYb244DgAorVauUYylEdaD5Y3osNh0J0yaNzJX81xl\no2fAT8Adb25T7P/t8wMebZrbLR7Hgq1nSrzq8Wy3IHbWkOyQ94WIiIiiBwNW8kubI3DITI6txD2x\n4JFFowAA4/tn6g4qnSOqzhqtl090Ja0p2WUvCVJW14Yvf3OOdNwSQyOszvWncs9cO0Gz/YMXjYTW\n7NXUBFOwutXtJOkc4fbX7j/Ml7YH9dKu37rmntn4148m4NNfzsKTV48PSV+IiIgoOjFgJb/c8PJG\nAMChiqYI96TryXckFspMitO9RnjD0WoAwKj8dAD2xEIFS0pQsKREalOYnYzBvVzJmV79tjRIPQ69\n37y7w+PYqPx03H3BUJXWgMEgYNG4PqrnjnopB0TeVTWFJiu4/CVCvmP9aoLje3/+KNdo+YCeKbhw\nTB5G5KWjh8ZILBEREXVNDFjJL9+faQQArHRLlkKdJzhGBkUAQ3PT8Pz1ExVZUzf+bo6i/Re/PgdH\nHCVJNpfWAgAa21wJc26fPQgAUDQmHwBw/bQBAIC3Np4ISf/DpSA7Bd8cqtY8/8dLR2OwymhdeiJn\nBbiTTz3P8hIIrjtUGbI+vPHTqXjgopG4fnoBAGDrA/Nw6bh8PO1lJJ2IiIi6DwasFJDe6czmGWzO\n5aj1rfagc/6o3khNcE3F7JVm/5o7p2PLE9Q4a6vK12n+a7U9Q26coyRJvkppmF+8uRXPrDrkcTza\n9Ur3TM7jlJJgws9mFXocd1/vS8DDn+yRtv9+5VjNdgmm0EwJBoAZg7Nx88xCGB1zuVMSTPjn1eOZ\n2I2IiIgAMGClAP1UJSCgznGOdbWbXYGVVWUtq3OJpk0UsfF3c5AUZ8RLP54EAFh7sMqzvWPoNinO\n88e9ZOdp/PWz/Z3reAg5p4n27aEMth+74iyv110+oa/Hsd4smeLh1fXHpO2xfbWzKL+ukrWaiIiI\nKBwYsFJAEuNCN+LSXaUn2tfzydf1DciyJ1MakZcuHTM4AlBRtI+67ntkAZLj7dc4p2yryVIpGaJm\n3cEqFCwpwaEK7XuFy6n6NgDAWLeSNL5G/Ewqo3PuJYC6q/s/2IV5j6+B2aoccXaOxLv7dsn5IUu6\nREREROQLA1YKyJoDoVvT1l1NHNADV0/upxg9dE6TlNfGfO3mqbhx+gBkp7rWHBoFjdS4Mhd4Kfki\nd91LGwAAcx//Wlf7cLh99iC8fvNU/GruEM02v52nnojJKZayI4fSmxuO42BFE9YfrlZ8PbVqrTIj\nOBEREUUS6zxQQCxWrgcMNpPRgOLLlVNdnVOC5SOnI/PT8YdFoxXtNGINhcQ4I4blpkklcOTO1LdF\n9ZTZ9MQ4jMrPwEwvNTjvnKMdzALAydqWYHcr5sh/bkUAkwZkAQDeu206TBojrEatOkFEREREYcAR\nVgrIz2cPjnQXuoWX1x3V1a7Dou8FgtEgqCYfKv50n1/9CjdvSZb08pYFt7todtRRBuxTgJ3fC0aD\nAJNGYGrQMXpPREREFCoMWCkgaqN0FHzu6wy16A0qzjS0YfuJOo/jH24/5Ve/Qq2+1Yx1sgRSvtas\nJulYU73JUfqnO5OXsTEZDNK+ySBIybkAYPXds6VtBqxEREQUSQxYSbf/7YyuoKY7uGRsH13tUhL0\nze6vae5AVVNHZ7oUFre/sUVaS6tHq9mqea6HYw2mlWtYFaPrFptNWtfrPu1Xvs8ZwURERBRJDFhJ\nt+oYCHS6mgkDtEuNyMWb/P9R/nzPGY9jB8uVmYFLq5r9vm8wbDvuOQrsrrS4CMt+frbPdpuXzgtG\nl7oEedDeZraizRHouydcijMaUFpchNLiIsXIKxEREVG4MWAl3Qb3So10F7odf0YF/RkJW3uwEre8\ntsXjeF2rWbE/+2+r0dRu0X/jIGnp0B4xlcvzkijKWQqISYNc5KV9apvNeHLlQQD2qeJy/JoRERFR\ntGCWYPKq3WKFyWCA0SBI9UH/edW4CPeq+3AGrGcP6umzrdEgwOaj1mjRWXlY9X0FNh2tURzvk5kE\nAKqJd8wWG9D5nEcB8/aiJD8zCct+Ph0j8zI8zn1w+9lodySjWjQuHx9tP4UOiy2g0eiuQv4CpLnD\ngsOV9hH0pjblS4mEuO77NSIiIqLowk8l5NWwpStw2+tbUFbXikXPfAMAmuUvKPjadWb/BQCzj2AV\nADKS4pAcb8STXx1SHE9LtL+MiDNG36+EQxVNXs9PHJCFpHjPpEuJcUZkJNnXr5ZW20vaDF36afA7\nGEP2nW6Qth/8aI+03TvD/kbCGczH6amTRERERBQG/FRCmkRHDdAv9pbjaKVrLWO/HsmR6lK34xwR\n05MFV484gyAl2lGjtlzRW/tYsUMlM3J3dLK2VfX4kNw0AMBITqMmIiKiKMMpwaSpptmVZKlNloU1\nMUjBE/mW6hj5HNAzOGWEjAaDYh2jk+PdBC575luPczYx9gNWsnvl21LV486p4K/cNBnfn2ns1tOm\niYiIKLowYCVN+067MsbKR1w4JTh8Zg/Nwd9+OBYXnZUXlPvFGQVFaROn/Y7swB0qdV9ZDqbrKKtT\nH2F11lrNTI7HtIG+10sTERERhQtfo5MmsyywWbxsp7StlpiHQkMQBFwxsW/QRrWNBgFtZv3rYgGW\nM+oO+DNNRERE0YoBK2mSTx2tbGyXtg2syxjVfjS1P/59wyQAwHu3TVeca2gzq13i1ae7TwelX4FK\nS+j8RJCz+npmESYXrlklIiKiaMUpwaSpd7p6jUtOCY5uY/pkYN7IXJQWF3mcy3eUr1EjaqxVPebI\nsBspI/PTO32Pj++YiYufWoectAjW54liAl9CERERUZTiCCtpsmoEMNmp/NAfzWYOydY8Z/QSmByu\ndJWP+fHZBdJ2ya7wjrDa3NbMDu+dFpT7GnxkSO4OklXK/xARERFFMwaspKrNbMXjXxxQPReNtTrJ\nJS0hTvPcl/vKNc8VPblO2p7lJegNNWdQ6ZylmpUSnBckJoPgEQx3N61mKzMAExERUUzhJxdS9fH2\nU/j6QGWku0EBMHj5qd5UWqt5rt3iSsYUyFrXYHFmMb58Ql8AwEVjg5Mh2SioZ0juTkQR6LAovwZT\nCrMi1BsiIiIi37iGlVTVtDAzbKwKRgKd3DT19cvh4BxhHdY7TXUdbqAMBqBCljyM7BaNy490F4iI\niIg0cYSVVGnV3pzQPzPMPSF/eQtY+3hJuiQ3ODc1WN3xmzM7dbBLrVQ1dbB8i4puPkuaiIiIohwD\nVlL118/2qx5/eNHoMPeE/BXvZY3xqrtnS9uXjM3XHMHskRyv2D9c2RS29Z8Wq33KqinIa6X7ZyWj\nsc2CpnZLUO8bK7ReQmllhyYiIiKKBgxYyS9ZKfG+G1FEeStREicrSTRtYE/Ndu7ZhOf8fQ0+2lHW\n+c7pcKq+DYByTW0wxBsNOF3fhtl/XRXU+8aKl9cdVT1exWnSREREFMUYsJJu9y4Y7rWOJ0XWt0vO\nxxs/neq1jSAI2HD/HKz87bm4Zko/AFC9xqAydXbjUe2ETcHUbrYCAIYGeVqy0RGsVzVFz/psURTD\nNsL5xV57hujBvVKx+u7ZUrKljGS+hCIiIqLoxYCVPGw7rh6YzBwcuVIn5Ft+ZhJm6Pg7yk1PxKCc\nVGkktn9Wsmq71bLpw4BndtlQ6XBMCU6MC27N0JKd4a0nq0fhfctx3UsbwvKsjaU1AOzfJwXZKZg/\nqjcAoG8PvoQiIiKi6MUsweTh28PVqsfH9M0Ic08oHNwDFmc5mYLsFORlJOK0Y4puqzk8az/NjoDV\n21rcruSbQ+o/b6GSlmD/tX/T2QUYmpvKF1FEREQU1brHJ0Lyy/YTdZHuAoWR+5rX4b3TpO3rpg0I\nd3dwoqYVQHDK85CnzOQ4APZp37OG5Hhd80xEREQUaQxYyYNzrZvT2sXn4dkfTYhQbyjcbp5ZKG3/\nYEIfadsWnhnBMDnWmoYywVd3zIyb5JhibWCASkRERDGEASv51C8rGQvH5EW6GxQm8oRL8lHOjaU1\nKLyvBIueXhfS5zvXygZ7Das8iVObOUzRt07OadCh5HwRkJEUF/JnEREREQULA1bycPXkftL2qPz0\nCPaEwuWRRaMAABP6ZyqOm62ukcia5g6IIrDjZH1I+yKtYTUF99fTgfImabuuNXoyBQPA0armkD9j\n4Wh7kqWbZhSE/FlEREREwcKAlTz0kE3FLLlrVgR7QuFy/fQClBYX4b+3z1Acj8TkUeca1lAmXZr+\n569Cdu9AmHSu17VYbShYUoK9pxr8fsa7m08CAHqmJvh9LREREVGkMGAlD0aucSOHXmnhD27SEu1Z\nbOOMwf0+fOtn04J6v2DSm/joX6sPAwAufHKtX/e3hGHKMREREVEoMGAlDyK6X0IaUmfSGOWc+/ga\nAMA97+3AriBPEbbaRCTGGYKevXb6oJ544upximMWqw3vbjoBqy2y3/NWnRmtalsCm8rcFqYaukRE\nRETBxoCVPFgi/OGdot+hiiacrm/Fe1tO4uIgJ2EyW0XEGULzq+mxFfsV+6+uP4bFy3bizY3HQ/I8\nbzpkQaTen7kRve1rypP8TEj1wtdH/GpPREREFC0YsJKHD7eVRboLFAO+dCt/FCxWmw3GIE8Hdiqr\na1Xsn3bs1zWHPwlTY5tZ2jZb9AWsSfH2QHXmkGy/nhXImlciIiKiaMCAlTyUN7QDAGYPy4lwTyia\nPfDRnpDc12wTdSch8tcst0DvxXVHAQD7yxtD8jxvTte3SdtN7RZd1/x7rX2kdE+Zf9Ow+2cl+9We\niIiIKFowYCVNL1w/KdJdoCiw5w/zMWd4r5Dd32YT8fGOU2jpsAdt9a1mtHRYQ/KshaPV6wmfkQWP\n4WITXaOqJqOAikbffdjpWC9c1eTfiLCzBu3K357r13VEREREkcaAlTQFuw4mxaaUBBOmD+rptc3X\nByoDvv/kR7/EXW9tw8gHPwMAlOw8HbKAdWBOirT9+Z4z0nbfHkkheZ438kRPb3x3DFMeXelz6q6z\n/0McAahezqnQWcnxPloSERERRRdGJETk0yVj86Vt92m1ALCmEwFrdRjXj04b6Aq8txyrxdWT+wEA\nphR6D8hDQT7Cuv5INQDg+zPeA9YrJvYF4P8a1kRHkibnGlgiIiKiWMGAlTwMy03DwtG9I90NiiLy\n0fZHLx3j9bw/bBHMSP3+lpOob7UnPnpnk/4swf+3vhQFS0rwsmP9a6Auf3a9tO1cN17jI3gvd0xd\n9neNrzMjcbxGmSIiIiKiaMVPL+TBYrPBEKKkNxSbMpPjcfcFQ3HruQPRv6dnAp9Zg/0b8XPqsHrW\nB+2dnoj0RFNA9/NHdXMHTtbap8ru8KOW7IOOZFN/LNkb9D75qj2bkmD/usQb/Rsp7bDaEGcU+HNN\nREREMYcBK3mwhjBLK8WuO84fgvsWjlA950/t3qZ2CyY+8gW+PVyF4Q+s8Difl5mIsf0yA+6nL/Jv\n7c5Mke0RgvWgbWbva3e/P2PPZvz6hmN+3ffZ1YdhtrK+MhEREcUeBqzkwWITYWTASn6w+hGwfn+6\nAdXNHfj75wdUz9tEwOBjpLEz7pozRNoe5wiMb5pR4Pd95Othg6WiwXumYOfIc2Vje9CfTURERBSN\nGLCSh5O1rTCGMGCg2NcnU5lV97HP9vu8Zs2BShQsKZEyCpc5puO6s4X4hUnfHq4pzc5v815piX7f\np2TXaZ8jov5KTjDhyZUHceXz6/HF3nIULClBwZIS6bw/I9lEREREXQEDVvKQlmhCbYs50t2gKHbt\n1P6K/X2nvWe3BYAbX94IAHjyq0MAgDMao4lWmxjSEdaLzlKvxarXlMIsaXvHiTq/r1+x+zQG379c\n9ZzFasPjXxzAxqM1+Nn/bVY5H1jAyin+REREFKsYsJIHq01EgUpiHSKnyQVZvhsFyCaKCGV85Szx\nAgDPrzni9/WDclw1UPWOeJ6oacGiZ77BhiPVuO31rZrX/XutdubhNQcqsUJWO9Yf+ZlJmFoYur8z\nIiIiolBhwEoeOiy2gMuUUPcwuk96SO5rttpgE6N7DXV1k2v96HNrDuu65ornvsWOE3W46oXvAnpm\na4dVGqEORI+UeFQ2cd0rERERxR5GJaRgs4mw2EQGrORVcrwJpcVFKC0uCup9Oyw2HChvwrHqlqDe\n1xebqH+qrTw78NqDVbqucdZZVTNTR0mg3324y+OYr5qtcjtO1CEnNUF3eyIiIqJooTsqEQTBKAjC\nNkEQ/ufYzxIE4QtBEA46/t9D47oFgiDsFwThkCAIS4LVcQqN+lb72tXGNkuEe0LdUYfFXpd1r441\nscHkT5bjzpTCUXPruQN9tvnv1jKPY3pHXI9WNQMANhyt8a9jRERERFHAn2G0XwLYJ9tfAmClKIpD\nAKx07CsIgmAE8AyAhQBGArhGEISRgXeXQs1stQcMhdkpEe4JdSePXjYaANDh+P4LN39GWIMt0PI4\nu8rqdbWzROhrSkRERBQMugJWQRD6AigC8KLs8CIArzq2XwVwqcqlUwAcEkXxiCiKHQBm9It8AAAg\nAElEQVTedlxHUcrq+ODOrKIUTq0d9vIw4R5ZdbL5McIa7KA61D9rkfqaEhEREQWD3hHWfwJYDED+\nSS1XFMXTju0zAHJVrusD4IRs/6TjGEUpZ9kMAwNW0umaKf1Ujze1W9BmtsJmE1FWp15z1alvD3td\n10ovaz2DyT2pU4dbuRibTURjm3ppp5Z2C4JZdUcQBOx48IKArj1R04KGNrPXUdSTGvVuiYiIiGKB\nyVcDQRAuAlAhiuIWQRBmq7URRVEUBKFTc+oEQbgFwC0A0L9/fx+tKVScUyONIayDSV3LWxvt76Qs\nVhtMRtc7sEVPr0NeRhKG9U7DS+u0y7UAgMlgv25jaXjWWRoEwCrbP12vDOoe/+IAnl51CDsevAAZ\nyXGKcx9uP6XYr2xsR05aYAmNihw1Yd2fodesx1ZJ93nm2gmqbeRJooiIiIhijZ4R1hkALhEEoRT2\nKb3nC4LwOoByQRDyAMDx/wqVa8sAyIdf+jqOeRBF8QVRFCeJojgpJyfHjz8CBZMz+YzJyICV/NNm\nUY7yHa5sxrpDVXhn0wmNK1yc32+bwhSwDs1NU+xnJikDxo922H9NOZOQeVPe0Ob1fMGSEs1z/7xq\nnLT93X1zpO3LJ/T1+Vy5kp2nNc8N7pWqeY6IiIgo2vkMWEVRvE8Uxb6iKBYAuBrAV6IoXgfgYwA3\nOprdCOAjlcs3ARgiCEKhIAjxjus/DkrPKSScAauBI6zkp/1nGqVteZDW1O4747RzhDVc5WxuOUeZ\nmffV9ccU+ydq7COuJ2p996fVbPXZRkucbES6d0aitH3/hcMDvqe7v372fdDuRURERBRunSm2WQxg\nniAIBwHMdexDEIR8QRCWA4AoihYAdwD4DPYMw++Korinc12mUGLSJQrUa+tLA7runvnDPEb0n7pm\nfOc75MW5Q/XN4thyrNbjWHZqPOQ/HoEmGP7jpaM9jr1y02Tct3A4egaxZuqmUvufQT6aS0RERBQr\nfK5hlRNFcTWA1Y7tagBzVNqcAnChbH85gOWd6aSav322H3WtHfjjpWOCfetu64EPd+O17+wjTQJH\nWMlP6w5VB3TdlMIsuH+35clGG0NBPrLpTbJKzdWqpg7F/pXPr1dd6wpo13c98qcLVRObzR7WC7OH\n9VIcKy0uwu8+2IU3NhzX1WctnBpMREREsagzI6wR9fSqQ3j9u859gCMlZ7AKcISV9PvbD8cCAP50\nmeeIoR4T+/dAYpwyMGzpCHyarR4pCfre1Z3VN1NXuzUHK1WP1zR3eBy7blp/v7Nw37twuJSg6dNf\nzsJz103063rAMzMyERERUSzwa4Q1WlQ3haf0RXeWlcrMoqRPQc9kAPYXHheM6g3RzzmyBoOAtETl\nr6JwrqGeOThbsQ71XVmSqCufX69o6/yzAsBVk/rhnc32tgkm9Xd/ZpVyMwtG5fndx/TEODxz7QQ8\nc619f0Reukeb3WX1GN0nQ/MegU5dJiIiIoqkmBxh/dNyJhEJNY7FkF7O0cK1B6sAAG1m7ZqgWuLd\nAr7OJDLS66GLR+IfV41FnFFAhyzD8eJlOzWvKZUlhappcY2e9uuRrNZcdVQzWAOdw9wyHV/01Dqv\n7YfmckowERERxZ6YDFjlIzgVPkpKUGCYJZj0cq/ZG0iw6b6m1N9R2kDcNKMQl43vC6NBwK6yeuw7\n3eDX9ZdP6CNtN7SZIYoiCpaUoGBJiZQZednWkx7XBWt9+Ge/PgelxUX4zbyhXtstHN0bQ3NTFTVy\niYiIiGJFTH6CkX+U/dPyfRHrR1dm4/xB0sl9FPHfa49otlWbygp4jrBOKsjqfMd0+nKfvYT0wifW\n+nXdjMHZ0vbSD3crRjhfc5TJeWzFfo/r9P5s3XbuIJw3zHc2Y1+1Ys1WUSobRERERBRrYnIN6wfb\nyqTtI1XNEexJ12I0CFJWUyZoIb3ko/FrD1bi2dWHNdt++stZAJR1WgEg3m30Lysl+tdQpyXG4Qfj\n++C/28pwqKJJca6mWXudvdZ6V3dLFuqrxWpRWScrV97Q5lE2iIiIiChW8LU7SeRBQ1+NNXlE7hYv\n2yFtX//SRl3X5LuVrdFbZiZW1DRrj3oWZKcE9VnLtrpe4NW3eD43Mc6AqkYmqiMiIqLY1LU+JXZR\ntc0dqApTZuSbZxaitLgoJka4KDrsLtO39rO0uEjaXnb72QCA3un2wDVaRvTbdK6/nT6wJwDtkeA2\ns9VjHe7IvHSUFhchOzWhc51041wvCwAVjco1/WarDZtKa5n1m4iIiGJWTAasZ/XVLt3QFY1/5AtM\n+uOXIX+OxWbzWEtI5MvNMwv9viY9MQ4AMG9krse5SMaud761TVe70/WtAICpjsDV3YCeyah0e8k0\nvr++mq7+kn+9ln64W3Hujje3AtD/UoGIiIgo2kTtGtamdgvijQbVAEo+fbBOZQoc+cditUGEPTmL\n+1pCIl8euGgk3t18Ao1tFsXxo3++EIX3LVe9JiXBhM1L5yIzKU469uEvZiAnLQFxEYxYv9hbrqud\ns7yNWsANAENz0zym554/vFfnOqdhzohcqd8bjtYozn22R9+fh4iIiChaRW10MvqhzzB06aeq51o6\nXNP2jte0YP3h6nB1q0sa/8gXGPeHzwFASrpE5I9XfzLF45iv8i3ZqQmKUivj+mWiT2YSeqUnerkq\n+C7QCDq9mSnLEKzGahM9ysjkpAV3KrDTSLfMy+EoCUREREQULlEZsPr6uJWbrvzgt/4IA9bOaGyz\noNnxEiA1MWoH3SmKje+nPt314ztmhLkn/iu+/Cy/r3ntZs8AXc5qExEny8z7xk+n4qy+oZkSfNec\nIYqZEVVNHSF5DhEREVEkRGXA6itiTY43hqcfXcyfl+/DjOKvvLbZfrwuTL2hrkRrNDVUQVowBZJg\nzNfocXlDG2yyajMzfIzIdobRIODyiX2k/YMVjSF7FhEREVG4RWXAKvqIWKcUZLldwClwejz/9RGU\n1bV6bbNiz5kw9Ya6mnEao6y/OG8Qnr9+Yph7459bzx2oevzZH03w6z6r7p4NAEiKN8Ji814fNZge\nvGiUtN0zxXPq8Ze/OSdsfSEiIiIKpqgMWH2xusWn3SVcbW63wGy14fqXNmDLsVq/rt1zql7atnGd\nKoXAL+cOkbafu84V6N0zfzjmj+odiS7p1m5WDy4XjsnTfY/S4iL0z7LXLz5c2QxbGF+kJcUbpZcC\naoFyYXZq2PpCREREFExRGbD6+pznHnB1l0RBXx+oxLHqZqw9WIV73t/h17U/fG69tN3UYfHSkigw\n02UlXhaM1h/oRYNXvi3VPPf+bdPRPysZUwvtMzsGZqcozj9x9Ti8fvNUAK56sumJJljC/HvJ5Hi2\nxf2NHqKnzi0RERGRv6Iyw47845bVJnp82Hp0+T7FvrULTwl+f8tJafvnb2yVto85ymroJc+s3NBq\nlupgEgVLYpwRpcVFke5G0E0qyMLXi8/TPL9oXB/FfmqCCWarKP3MPXddeKZDOwPk/WcaMVY2Pfua\nKf3C8nwiIiKiUIjKEVY5PevAuvIU17vfUx9J7cyo8l9W7Je23b92y++aFfB9iWKVcypvMJiMAqw2\nm1RexmwNz1rWe5ftBAAsdvwfAOJNBtaqJiIiopgWlQGrvI6gnsAs3FPvwmXf6Yag3OfdzScU++1m\n12jrJztPKc6NzFfWdCTqDobmpknbvmqs+lLXYsaaA5Vot9gD1ezU0NRfVXuu0/JdpwEAHRYbMpM5\nm4KIiIhiV5QGrK5ts8p6LHe7y+p9tolFC59YG5T7LH5/p2K/VRawtsqmChN1V0myUlkDc+xrVM/q\nmxHw/UqrW6TfXfGm8Kwf/cdVY6Xt29/YimPVzQCAtzae0LqEiIiIKOpFZcDa0OYaKbDomE53ur4t\nlN2JWkcqm/D8msOKEWk99p9x1Wl86qtD0vbsYTlB6xtRLElPdC3njzMaUFpchI/vmNmpeza12ZOb\nxRvDUzf6svF9FfsVje1heS4RERFRKEVlwCoPQN0/dO095TlN9mSt99qiXdX5f1+DP3/6PbafqPPr\nuorGdilolddl7ZEcH9T+EcWK2cN6SdsvrTsalHs6ZzIIEUrQK88MTkRERBSrojJglXNPWCKf/huu\n7JvRrralw+9rDlc2eRxLjg/PSBBRtJlc0CPo93QmN09NiGwydmc5HiIiIqJYFPUB6zubXOuvyupa\nFRkw+2QmRaJLUaem2f8soLc7SuSMk5W/GNs3U6s5UZeWYAr+y5rXvzsGAIgzRfbXrLzEDREREVGs\nifqA9Y0Nx6XtigblWtWC7OCVoohl8To/EP/th2M9jv1wkn3d21s/myZtE3U3oZi2u/W4fap+vDGy\nv2Z/OJE/10RERBS7oj5glbPJkgv9eu5QGA2uT5kdlvDUOoxG//76iOa5lg6LtD2+v+dIy/HqFgDA\niLw0CJFabEcUYaH81k+IC9+v2WGy8jxOejKtExEREUWrqA9Yn7h6nLQtD6iOVDXBINtvs3Tt8iwX\njMzVPLfLS1mfSlnSqkE5qR7nnevrUiK8zo4okoI5JXiQoyyOU2p8+H623r5lmscx+UsrIiIiolgT\n9QGrfFRVHqD2SktQ7Hc1v/tgl2L/hRsm4c2fTvX7PluP12qe+2JvuZTUymToul9LonA6e1C2Yt8Q\nxp+tHimemb7TEuPC9nwiIiKiYIv6gPW7wzXStvxj31WT+ynWbtpsXWvam3ztrlO7jpq07p6W1Vl1\n95t3tqPdakO8ycDpwEQOm5fO7dT1d18wLEg9CcwN0wco9ofmes6sICIiIooVUR+wvrPZlSVYHlO5\njxpYuljAqmbfac8atL4crmzWPNfYbsGr35ZydJVIJjs1oVPXZyRHdkTz4UWjFft8GUVERESxLOoD\nVrnqZle90Ywk5YdCaxcOWGcOtk8xLBqTF/A9nKMsj1yq/DDbZrahpaNrr/8l0uOWcwZiVH56pLtB\nRERERDIxk2nnkx2ncOdb26T9xDh7kpS/XD4G9y7bFdMB67OrD2Pm4GyM6ZvhcW7Fr2ZheG/7h+j0\nTqxF65liHzUaq/IMIgLuv3BEpLtARERERG6idoQ1OV6ZtVMerMoZDfY/QqwGrKIo4i8rvscdb21V\nPR8nq+Got96qmocuGQkAGKpS9oKIiIiIiCgaRWXA2iM5HnsfXiDtN7drl2VoM9unszbHaOmGe97f\nCQA4Vt2CEzUtHueNsvVn8uDVX85R2sQ4I0qLiwK+DxHpd87QnIg+PzvVM2swERERUSyJyoDV3aiH\nPtM8t/TD3QCABf9cG67uBNX7W05K27MeW+Vxvle6KwFMnFE7eUqTl6BeTXpizMwGJ4o5aY6frzvO\nGxyR5zuf++t5QyPyfCIiIqJgicqoRV57tbuaNzIXJ2tbkRzv+isSBAFH/3yhlPVTFEUU3rccAGC1\nqn/NZg3JVg1md/5+PgqWlISg50S06/fzI/r8u+cPw93zI1teh4iIiCgYonKEtb7VDADQqrYyTLYO\ns7MlKKJVh8WmumZVXqJCvq0V5JutNsQZovKvmYiIiIiIyKuojmT2PWJfx3qP20jBB784W9oe2YXK\nUDhLz1htItYcqNRVH/X3F9uTKWkFrKfq2oLXQSIiIiIiojCK6oA13pFkqN2srBMqnyY7OoYDVptb\nZuMD5U0AgBEPrgAAbDlW6/MecSbtLMk2m4jjNS3YWFrT2a4SERERERGFXVQGrM4prM4pr09+dUiz\n7cVj88PSp1DosNrUj1vUj6txjsJaVALWVrdAX8vrN0/V/TwiIiIiIqJwicqA1Z8ll+lJcaHrSIi1\n+xGYavnb5wcAAD95ZZPHuV1l9brukZveNdcBExERERFRbIvKgNV9Oeb0gT2l7aeuGa841yczKRxd\nCgmz2whrIH+WysZ2AMD3Zxo9zumZUgwAQ2RJrIiIiIiIiKJFVAasCbLsuEN6pWLHyTppf1y/TM3r\n1KbSbjhSjbP/vBLNftYpDQdnf4t/MAYAUFbX6vc9slPjFfvvbT6Bi56y16Td7Fi7qpW76et7zsPL\nP57k9zOJiIiIiIjCISoD1nzZSKPRICAxzijtC14S59Y0d3gcu+qF73Cqvk11BDLSnCOs7uVrisbk\nAQBevMF3MLlgdG/FPe55fyd2lzUAAFbtrwQA/GRGoeq1/Xsm4/zhuQH0nIiIiIiIKPSiMmCVB3CZ\nyXGKQFSjegsA4It95ZrnElRqmkZaQ6t91Nf553WOhNa3mtErLQFzR/oOJu88fwgA+2itKPviFCwp\nkbb79IjdadNERERERNR9RV8U5+a7I/pLspi9JDEyeBuajRBnlmCL1R5oOhP99kiJVx0tVpOe6Eo6\nVdnUrtpmDkdRiYiIiIgoBkV9wOpOrd6oU12rWbG/9bgr6dDGo9Uh61OgLI6ANSfNlaX3/9aXwmK1\noTA7Rdc94oyuQHy1Ywqwu9REk+pxIiIiIiKiaBb1AeuE/sokS3mZiZpt/7fjlGL/B//6Vtr+/Sd7\ng9uxIHAG30ZZVqQHP9qDMw1timPeyNstfn+nzzZERERERESxIuoD1svG95G2n7l2AhJMRs22HdbO\n1zUNJ7MjYJWPkgL29ahxRn1/NYKOqc4mBqxERERERBSDoj5glQdkvuIueV3TNrM1VF0KGqvN3l+j\nQfnXYLbagjoqGo0Jp4iIiIiIiHyJ+khm6Ye7pe0BPdXXdV47tT8AV/IiAGj3koAp1PaeasAH2076\nbLep1L7G1mQQcNu5g6TjNc1mj1HXzjDpHK0lIiIiIiKKJjEVyciTE8lNKcgCAEx2/B8Arn9pQ1j6\n5O6zPWdw4ZNr8et3duBPy/d5bfvs6sMAgDijAc3tFum4TRTR2GbRuoyIiIiIiKhb6BoBa6E9UJ0x\nJFs6tvNkfVj6JIoiKhrbpP1DFU3S9ur9FZrXNckC1OzUeIzMT5f2rTYRuenayaXcPbJolOa5L39z\nru77EBERERERRZOoD1jH9cv02caZVOgB2fThcLnqhe8w5dGV+HzPGQDAsepm6dyB8iatyzD6oc+k\n7ZQEE07UtEj79a1mxPux7tRsVS/1s+znZ2Nwr1Td9yEiIiIiIoomUR+w6kkYFMk1mhuP1gAAbnlt\nCwDg3c2+1666SzAZ8P2ZRsWxpDjtbMjuSmVBMgD8ZEYhin8wBhMH9PC7L0RERERERNHCFOkO+DK4\nVyo2OIJCLdFSZ7RgSYnPNte/tAFrD1YpjgmCgPpWs+JYL43pz2p6ZyinD18+sQ9G5Wfovp6IiIiI\niCgaRf0I663n2LPnjshL12zjLaPu4gXDgt6nznAPVp3y3IJOf6YEXzWpn2KfwSoREREREXUFUT/C\n2r9nMh674izMHpqj2cZk0A7ufjRlAFrarXh2zeGg9uuTHafw3hb/p/9qWbJwOP6387S0788050Q/\npg8TERERERHFiqgPWAHgSrcRRHdqo5Gj8tOx51QDMpLjYDAIsNpEiKIIQQjO9OE739rms83NMws9\njpkMAiw2V5Kkt2+ZBgDok5mkaNfYppwi7E1Kguuv8ZFLR+u+joiIiIiIKJrFRMDqj3aLFQkmI/r1\nSIbFkT3X6AhSbSLgZfZw0KUmeH55e6bGo7yhXdqfNrAnAPs61gcuGolH/rcXAJCfkeRxrTelxUWd\n6CkREREREVH0ifo1rP664tn1WHuwElZRhHMwta61A4A9mA0nm+hZbkYerLqPhhZ/uk/aNkRJIiki\nIiIiIqJI6XIjrLvK6nH9SxsVx7JT7Rl3teqVhkKc0T4N2Zvrpw3QPBfOkWAiIiIiIqJo1OVGWNU8\ns+oQAOD9ICZJ8ubxK8fCIPgOWN3dPnuwtG2MYG1ZIiIiIiKiaNAtoqKWDvtU4COVTSF/VmF2Cn4w\noS8AoKKxXXHO15TkCQN6SNvGICWHIiIiIiIiilXdImB1emPD8ZA/46459lHSdosN7iHnu5u9j/CW\n7DwlbXOAlYiIiIiIujuGRUF0/4XDcdl4++hq7/REj8RJpVXNrm2VrL51La5SNhuO1oSol0RERERE\nRLGBAWsQjevnmtJrttrQ0KqspWrykUmp6Kw8afv7043B7RwREREREVGMYcAaoDRZjdWReen4350z\nMaUwSzpW3dyBz/eWS/s2m4h4H/N8LxmbL21nJscFsbdERERERESxp8uVtQkXeY3V5g4LRvfJ8Np+\n4P3Lpe1BOSmqbQRZoqXhvdM72UMiIiIiIqLY1mVGWNcuPs/j2AvXTwQAHHx0IQDgiol9g/Y8i6xk\nTXqif6OhH/xihs82107t73efiIiIiIiIupIuM8LaLyvZ49jgXqkAgDijARlJcUiJNwbteWarTdpO\nS9T+MrZbrGhqsyiO6Qtw/avhSkRERERE1NV0mRFWNQNzUqXt+lYzXl1/LGj3tumMJ2uaO7DlWK3f\n9x8k6zsREREREVF31KUD1nBJMGl/Gcsb2hXTh/WSr2clIiIiIiLqjrrMlOBIuXJSX9w9f5jm+b9/\nvh9TCrI0z7v746WjMSKPCZeIiIiIiIi6XcBqsdpg8lFexpcOi2v96mNXjFVtkxJvRHOHFWsPVmHt\nwSrd975u2oBO9Y2IiIiIiKir6FJTgsf3z/TZ5smVBzv9nH+vPeKzze+KRkrbv5k3VNqeM7xXp59P\nRERERETUHXSpgPWD22fgT5eN8dqmf097DdQTNS1Ysmwnyupa/X5OaVWzzzbyWqslO08DAFbfPRsv\n/Xiy388jIiIiIiLqjrpUwAoA5w3P8Xr+9x/vAQDMfXwN3t50Apc8tc7vZ+jJoTQkN03a3l/eCAA4\n09Dm97OIiIiIiIi6qy4XsOZlJHk939Rur4na7liHWt3c4fczth6vdTwrUbNNVkq8xzGLlbVViYiI\niIiI9OpyAaseJ2tbAr526/FaHHVMCS4ak+fXtRabzXcjIiIiIiIiAtBFA9YDf1yIg48uVBy7b+Fw\nabu8oT3gex+rdq1fvWJSX7+u7e1lRJaIiIiIiIiUumRZm3iTZxw+1LGmdGBOCi5/9tuA7/3KN6XS\ntlEQ/Lq2X4/kgJ9LRERERETU3XTJEVY1UwdmAQCunNSvU/cpq3MlTjIYvAesiXHKL29KQpd8P0BE\nRERERBQS3SZgNThGQ4s//b5T97lqsmsasK8R1kE5qZ16FhERERERUXfWbQJWo4/RUL1EWaJfX/cU\nmRSYiIiIiIgoYN0nYPVzvamWFXvOSNsdVu9Zf9VK2xAREREREZE+3SZg9bXeVK8jla4sweUNbV5a\nAk9cPc71/OA8noiIiIiIqNvoNgGrN1abvrm77RarYj89Mc5r+56pCdL28l/O8r9jRERERERE3RgD\nVgAWm/epvU6f7jqj2B+Zl677GQI4xEpEREREROQPBqwAapo7dLX71TvbFfv+TDMWwQxMRERERERE\n/mDACmD2X1eH7N4Dc1IAAAU9U0L2DCIiIiIioq7IFOkORMrVk/vh7U0nAADtFn1TggPx1W9nh+ze\nREREREREXVm3GmEd1y9T2n570wkkmOx//OxU/8vPzB3RK2j9IiIiIiIiIk/dKmDNSUtQ7M9xBJ1V\nTfrWsMr9+4ZJQekTERERERERqetWAWtinFGxP6F/j4DvJQjM+ktERERERBRK3SpgXbmvXLF/7tAc\n3de+ueG4tL334flB6xMRERERERGp61YBa2G2MlNvRnKcrut2l9Xj/g92SfvJ8d02VxUREREREVHY\ndKuA9X93zpS2v77nPOSkJnhp7bL3dEOoukREREREREQafA4VCoKQCOBrAAmO9u+LoviQIAhjATwH\nIBVAKYAfiaLoEdkJglAKoBGAFYBFFMWIZSuSrzvtlZ6gex3q4vd3hqpLREREREREpEHPCGs7gPNF\nURwLYByABYIgTAPwIoAloiiOAfABgHu83OM8URTHRTJYdWc0KINVURQj1BMiIiIiIiJS4zNgFe2a\nHLtxjv9EAENhH3kFgC8AXB6SHgZZn8wkAIDJEbDeM38YAKC5wxqxPhEREREREZEnXWtYBUEwCoKw\nHUAFgC9EUdwAYA+ARY4mPwTQT+NyEcCXgiBsEQThls52uLOeu24ilhaNkKYDd1hs/9/e3cfqWdZ3\nAP/+SmkBqaW8dZUSBksXQ1ReJEQTJJvOSiGZMzEZyxLJ3OKiZnFLlg0jyfxzL9GFZYmGZVvYsog6\nNWMvbsFJIssyajFQymilINssYBHFYnxhttf+eK5z9nA4pxyE89w3PJ9PcuW5nuu+7uf8zp1fr/R3\n7pcnSfK9H/5oyLAAAABYYlUFa2vtaGvt4iTbk1xeVa9J8u4k76uqu5JsSvL0Crtf0ffdleT9VXXl\ncpOq6j1Vtaeq9jz++OPP+xdZrddu35xfe9MFi+9P2TD5btYjP1CwAgAAjMnzekpwa+3JJLcnuaq1\ntr+1trO19vokn0jy4Ar7HOqvhzO51/XyFebd1Fq7rLV22Vlnrf77UV+om770UJLk5n9/eMU5Oy/c\nutg/9/ST1zokAAAAsoqCtarOqqrTev/kJG9Nsr+qzu5j65LckMkTg5fu+4qq2rTQT7Izyb4XL/wX\n7qlVXAq89ZUnLfbv+J03r2U4AAAAdKs5w7otye1VtTfJlzO5h/UfkvxSVX01yf4kjyT5yySpqldV\n1T/1fbcm+bequifJ7iT/2Fr75xf7l3ghFi4JXriXdTmr/PYbAAAAXkTP+T2srbW9SS5ZZvzGJDcu\nM/5Ikqt7/6EkF73wMNfOul6NHnz8uyvOeeK7K92eCwAAwFp5Xvewvhz9/EWvSpLc9V/fXnHOSSdO\nzsK+92d+aiYxAQAAoGDNNa/bttg/dqwtO2f9usrZmzbmd6969azCAgAAmHtzX7Bu2/z/D1T6vVvv\nW3bO0dayfp0bWQEAAGZp7gvW7VtOWex/cf/hZeccay3rFKwAAAAzNfcF67RDT35/2fFjx9riw5kA\nAACYDQXrKhxtyQnOsAIAAMzUc36tzbx77Ds/yN/f88jQYQAAAMwdZ1iXuP/RIz7sS7wAAAbeSURB\nVM94f+fXnhgoEgAAgPmmYF1i1413POP9Bz9770CRAAAAzDcFa5IPvGXHitu+9/TRGUYCAADAAgVr\nkt9660/nXW88b+gwAAAAmKJg7W645sJlx3ecfeqMIwEAACBRsC7asH75Q3HeGafMOBIAAAASBetz\n2nzyhqFDAAAAmEsK1im7XvMTSZJbdv939j92JN9/+mg+85WvDxwVAADAfFo/dABj8vl9jyVJru9f\nZfOmHWcOGQ4AAMBcc4b1OO544JtDhwAAADC3FKyrdNH2zUOHAAAAMFdcErwKH/vlS/Ozrz576DAA\nAADmijOsUy4+97Rlx3e9dltOOvGEGUcDAAAw3xSsUz7162981ljVAIEAAACgYJ22Yf2zD0drAwQC\nAACAghUAAIBxUrACAAAwSgrWJf74Fy8aOgQAAACiYH2Wd1yyPXtu+LmhwwAAAJh7CtZlnHnqxsX+\nH73zdQNGAgAAML/WDx3AWD38+9cMHQIAAMBcc4YVAACAUVKwAgAAMEoKVgAAAEZJwQoAAMAoKVgB\nAAAYJQUrAAAAo6RgBQAAYJQUrAAAAIySghUAAIBRUrACAAAwSgpWAAAARknBCgAAwCgpWAEAABgl\nBSsAAACjpGAFAABglBSsAAAAjJKCFQAAgFFSsAIAADBKClYAAABGScEKAADAKClYAQAAGCUFKwAA\nAKOkYAUAAGCUqrU2dAzPUlVPJTkwdBzMvTOTfHPoICBykfGQi4yFXGQs5OKP57zW2lmrmbh+rSP5\nMR1orV02dBDMt6raIw8ZA7nIWMhFxkIuMhZyce25JBgAAIBRUrACAAAwSmMtWG8aOgCIPGQ85CJj\nIRcZC7nIWMjFNTbKhy4BAADAWM+wAgAAMOdGVbBW1VVVdaCqDlbV9UPHw0tXVT1cVfdW1d1VtaeP\nnV5Vt1XVA/11y9T8D/a8O1BVb5saf33/nINV9SdVVX18Y1V9so/fWVU/ObXPdf1nPFBV183ut2YM\nquovqupwVe2bGhs096rq/D73YN93w1ofB4a3Qi5+uKoO9bXx7qq6emqbXGRNVNW5VXV7Vf1nVd1X\nVR/o49ZGZuo4uWhtHLPW2ihakhOSPJjkgiQbktyT5MKh49Jemi3Jw0nOXDL2h0mu7/3rk/xB71/Y\n821jkvN7Hp7Qt+1O8oYkleTzSXb18fcl+XjvX5vkk71/epKH+uuW3t8y9PHQZpp7Vya5NMm+qbFB\ncy/Jp5Jc2/sfT/LeoY+TNlgufjjJby8zVy5qa5mL25Jc2vubkny155y1URtLLlobR9zGdIb18iQH\nW2sPtdaeTnJLkrcPHBMvL29PcnPv35zkF6bGb2mt/bC19rUkB5NcXlXbkryytfYfbbKC/NWSfRY+\n62+TvKX/Ze1tSW5rrX2rtfbtJLcluWqtfzHGo7X2pSTfWjI8WO71bW/uc5f+fF7GVsjFlchF1kxr\n7dHW2ld6/6kk9yc5J9ZGZuw4ubgSuTgCYypYz0nyP1Pvv57jJxAcT0vyhaq6q6re08e2ttYe7f3H\nkmzt/ZVy75zeXzr+jH1aaz9K8p0kZxzns5hvQ+beGUme7HOXfhbz6Teqam9NLhleuARTLjIT/fLI\nS5LcGWsjA1qSi4m1cbTGVLDCi+mK1trFSXYleX9VXTm9sf81zCOymTm5x8A+lsmtNxcneTTJR4YN\nh3lSVacm+UyS32ytHZneZm1klpbJRWvjiI2pYD2U5Nyp99v7GDxvrbVD/fVwks9lcsn5N/olHOmv\nh/v0lXLvUO8vHX/GPlW1PsnmJE8c57OYb0Pm3hNJTutzl34Wc6a19o3W2tHW2rEkf5bJ2pjIRdZY\nVZ2YSYHwN621z/ZhayMzt1wuWhvHbUwF65eT7OhPydqQyU3Ktw4cEy9BVfWKqtq00E+yM8m+TPJp\n4Yls1yX5u96/Ncm1/alu5yfZkWR3v0zpSFW9od9f8K4l+yx81juTfLH/dfhfkuysqi39cpKdfYz5\nNlju9W2397lLfz5zZqE46N6RydqYyEXWUM+dP09yf2vto1ObrI3M1Eq5aG0cuVk+4em5WpKrM3la\n14NJPjR0PNpLs2VyScc9vd23kEuZ3CPwr0keSPKFJKdP7fOhnncH0p/y1scvy2TRejDJnyapPn5S\nkk9ncvP97iQXTO3z7j5+MMmvDH08tJnn3ycyuZzofzO5D+VXh869/m9idx//dJKNQx8nbbBc/Osk\n9ybZm8l/qrZNzZeL2lrl4hWZXO67N8ndvV1tbdRm3Y6Ti9bGEbeFAwsAAACjMqZLggEAAGCRghUA\nAIBRUrACAAAwSgpWAAAARknBCgAAwCgpWAEAABglBSsAAACjpGAFAABglP4PRrJ41QVyat0AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25281ccb5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['midprice'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7QAAAJCCAYAAADwe4seAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCrn1MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252ac873080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['dmidprice'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "\n",
    "In this section we define our feature set. Our first regressor is VOI (Shen, 2015)\n",
    "http://eprints.maths.ox.ac.uk/1895/1/Darryl%20Shen%20%28for%20archive%29.pdf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dbidsize = df['bidsize'].diff()\n",
    "dbidprice = df['bidprice'].diff()\n",
    "cvbid = np.where(dbidprice > 0, df['bidsize'], 0)\n",
    "cvbid = np.where(dbidprice == 0, dbidsize, cvbid)\n",
    "cvbid = np.where(dbidprice < 0, 0, cvbid)\n",
    "dasksize = df['asksize'].diff()\n",
    "daskprice = df['askprice'].diff()\n",
    "cvask = np.where(daskprice < 0, df['asksize'], 0)\n",
    "cvask = np.where(daskprice == 0, dasksize, cvask)\n",
    "cvask = np.where(daskprice > 0, 0, cvask)\n",
    "df['voi'] = cvbid - cvask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>bidprice</th>\n",
       "      <th>bidsize</th>\n",
       "      <th>askprice</th>\n",
       "      <th>asksize</th>\n",
       "      <th>midprice</th>\n",
       "      <th>dmidprice</th>\n",
       "      <th>voi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-04-18 00:00:00.159</td>\n",
       "      <td>39.79</td>\n",
       "      <td>7</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "      <td>39.795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-18 00:00:00.739</td>\n",
       "      <td>39.79</td>\n",
       "      <td>6</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>4</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>3</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>3</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "      <td>39.790</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>6</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>21</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>9</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-04-18 00:00:01.664</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-04-18 00:00:01.664</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-04-18 00:00:01.688</td>\n",
       "      <td>39.79</td>\n",
       "      <td>2</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-04-18 00:00:01.826</td>\n",
       "      <td>39.79</td>\n",
       "      <td>2</td>\n",
       "      <td>39.8</td>\n",
       "      <td>6</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  bidprice  bidsize  askprice  asksize  midprice  \\\n",
       "0  2016-04-18 00:00:00.159     39.79        7      39.8        3    39.795   \n",
       "1  2016-04-18 00:00:00.739     39.79        6      39.8        3    39.795   \n",
       "2  2016-04-18 00:00:01.354     39.79        5      39.8        3    39.795   \n",
       "3  2016-04-18 00:00:01.354     39.79        5      39.8        4    39.795   \n",
       "4  2016-04-18 00:00:01.354     39.79        4      39.8        4    39.795   \n",
       "5  2016-04-18 00:00:01.354     39.79        3      39.8        4    39.795   \n",
       "6  2016-04-18 00:00:01.354     39.79        3      39.8        5    39.795   \n",
       "7  2016-04-18 00:00:01.661     39.79        1      39.8        5    39.795   \n",
       "8  2016-04-18 00:00:01.661     39.78       20      39.8        5    39.790   \n",
       "9  2016-04-18 00:00:01.661     39.78       20      39.8        6    39.790   \n",
       "10 2016-04-18 00:00:01.661     39.78       20      39.8        7    39.790   \n",
       "11 2016-04-18 00:00:01.661     39.78       21      39.8        7    39.790   \n",
       "12 2016-04-18 00:00:01.661     39.78       22      39.8        7    39.790   \n",
       "13 2016-04-18 00:00:01.661     39.78       22      39.8        8    39.790   \n",
       "14 2016-04-18 00:00:01.661     39.78       22      39.8        9    39.790   \n",
       "15 2016-04-18 00:00:01.661     39.78       22      39.8        8    39.790   \n",
       "16 2016-04-18 00:00:01.664     39.79        1      39.8        8    39.795   \n",
       "17 2016-04-18 00:00:01.664     39.79        1      39.8        7    39.795   \n",
       "18 2016-04-18 00:00:01.688     39.79        2      39.8        7    39.795   \n",
       "19 2016-04-18 00:00:01.826     39.79        2      39.8        6    39.795   \n",
       "\n",
       "    dmidprice  voi  \n",
       "0         NaN  0.0  \n",
       "1       0.000 -1.0  \n",
       "2       0.000 -1.0  \n",
       "3       0.000 -1.0  \n",
       "4       0.000 -1.0  \n",
       "5       0.000 -1.0  \n",
       "6       0.000 -1.0  \n",
       "7       0.000 -2.0  \n",
       "8      -0.005  0.0  \n",
       "9       0.000 -1.0  \n",
       "10      0.000 -1.0  \n",
       "11      0.000  1.0  \n",
       "12      0.000  1.0  \n",
       "13      0.000 -1.0  \n",
       "14      0.000 -1.0  \n",
       "15      0.000  1.0  \n",
       "16      0.005  1.0  \n",
       "17      0.000  1.0  \n",
       "18      0.000  1.0  \n",
       "19      0.000  1.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>bidprice</th>\n",
       "      <th>bidsize</th>\n",
       "      <th>askprice</th>\n",
       "      <th>asksize</th>\n",
       "      <th>midprice</th>\n",
       "      <th>dmidprice</th>\n",
       "      <th>voi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-04-18 00:00:00.159</td>\n",
       "      <td>39.79</td>\n",
       "      <td>7</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "      <td>39.795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-18 00:00:00.739</td>\n",
       "      <td>39.79</td>\n",
       "      <td>6</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>3</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>5</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>4</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>3</td>\n",
       "      <td>39.8</td>\n",
       "      <td>4</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-04-18 00:00:01.354</td>\n",
       "      <td>39.79</td>\n",
       "      <td>3</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>5</td>\n",
       "      <td>39.790</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>6</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>21</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>9</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>22</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-04-18 00:00:01.664</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>8</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-04-18 00:00:01.664</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-04-18 00:00:01.688</td>\n",
       "      <td>39.79</td>\n",
       "      <td>2</td>\n",
       "      <td>39.8</td>\n",
       "      <td>7</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-04-18 00:00:01.826</td>\n",
       "      <td>39.79</td>\n",
       "      <td>2</td>\n",
       "      <td>39.8</td>\n",
       "      <td>6</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  bidprice  bidsize  askprice  asksize  midprice  \\\n",
       "0  2016-04-18 00:00:00.159     39.79        7      39.8        3    39.795   \n",
       "1  2016-04-18 00:00:00.739     39.79        6      39.8        3    39.795   \n",
       "2  2016-04-18 00:00:01.354     39.79        5      39.8        3    39.795   \n",
       "3  2016-04-18 00:00:01.354     39.79        5      39.8        4    39.795   \n",
       "4  2016-04-18 00:00:01.354     39.79        4      39.8        4    39.795   \n",
       "5  2016-04-18 00:00:01.354     39.79        3      39.8        4    39.795   \n",
       "6  2016-04-18 00:00:01.354     39.79        3      39.8        5    39.795   \n",
       "7  2016-04-18 00:00:01.661     39.79        1      39.8        5    39.795   \n",
       "8  2016-04-18 00:00:01.661     39.78       20      39.8        5    39.790   \n",
       "9  2016-04-18 00:00:01.661     39.78       20      39.8        6    39.790   \n",
       "10 2016-04-18 00:00:01.661     39.78       20      39.8        7    39.790   \n",
       "11 2016-04-18 00:00:01.661     39.78       21      39.8        7    39.790   \n",
       "12 2016-04-18 00:00:01.661     39.78       22      39.8        7    39.790   \n",
       "13 2016-04-18 00:00:01.661     39.78       22      39.8        8    39.790   \n",
       "14 2016-04-18 00:00:01.661     39.78       22      39.8        9    39.790   \n",
       "15 2016-04-18 00:00:01.661     39.78       22      39.8        8    39.790   \n",
       "16 2016-04-18 00:00:01.664     39.79        1      39.8        8    39.795   \n",
       "17 2016-04-18 00:00:01.664     39.79        1      39.8        7    39.795   \n",
       "18 2016-04-18 00:00:01.688     39.79        2      39.8        7    39.795   \n",
       "19 2016-04-18 00:00:01.826     39.79        2      39.8        6    39.795   \n",
       "\n",
       "    dmidprice  voi  \n",
       "0         NaN  0.0  \n",
       "1       0.000 -1.0  \n",
       "2       0.000 -1.0  \n",
       "3       0.000 -1.0  \n",
       "4       0.000 -1.0  \n",
       "5       0.000 -1.0  \n",
       "6       0.000 -1.0  \n",
       "7       0.000 -2.0  \n",
       "8      -0.005  0.0  \n",
       "9       0.000 -1.0  \n",
       "10      0.000 -1.0  \n",
       "11      0.000  1.0  \n",
       "12      0.000  1.0  \n",
       "13      0.000 -1.0  \n",
       "14      0.000 -1.0  \n",
       "15      0.000  1.0  \n",
       "16      0.005  1.0  \n",
       "17      0.000  1.0  \n",
       "18      0.000  1.0  \n",
       "19      0.000  1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAJCCAYAAAAWUkBaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252b256a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['voi'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7QAAAJCCAYAAADwe4seAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCrn1MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252f8822940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['dmidprice'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\Win64\\Anaconda\\V4.4.0_3.6\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>dmidprice</td>    <th>  R-squared:         </th>  <td>   0.015</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.015</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>4.134e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 14 Nov 2017</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:09:29</td>     <th>  Log-Likelihood:    </th> <td>1.4844e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>2740313</td>     <th>  AIC:               </th> <td>-2.969e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>2740312</td>     <th>  BIC:               </th> <td>-2.969e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>voi</th> <td> 6.466e-05</td> <td> 3.18e-07</td> <td>  203.319</td> <td> 0.000</td> <td>  6.4e-05</td> <td> 6.53e-05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>654181.996</td> <th>  Durbin-Watson:     </th>   <td>   2.020</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>42315103.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 0.004</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>22.251</td>   <th>  Cond. No.          </th>   <td>    1.00</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              dmidprice   R-squared:                       0.015\n",
       "Model:                            OLS   Adj. R-squared:                  0.015\n",
       "Method:                 Least Squares   F-statistic:                 4.134e+04\n",
       "Date:                Tue, 14 Nov 2017   Prob (F-statistic):               0.00\n",
       "Time:                        09:09:29   Log-Likelihood:             1.4844e+07\n",
       "No. Observations:             2740313   AIC:                        -2.969e+07\n",
       "Df Residuals:                 2740312   BIC:                        -2.969e+07\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "voi         6.466e-05   3.18e-07    203.319      0.000     6.4e-05    6.53e-05\n",
       "==============================================================================\n",
       "Omnibus:                   654181.996   Durbin-Watson:                   2.020\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         42315103.946\n",
       "Skew:                           0.004   Prob(JB):                         0.00\n",
       "Kurtosis:                      22.251   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "df.dropna(inplace = True)\n",
    "model = sm.OLS(df['dmidprice'], df['voi']).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7QAAAJCCAYAAADwe4seAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252daabaf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.voi, df.dmidprice);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127715"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['dmidprice'] != 0]\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>dmidprice</td>    <th>  R-squared:         </th>  <td>   0.137</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.137</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>2.032e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 14 Nov 2017</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:09:57</td>     <th>  Log-Likelihood:    </th> <td>5.0451e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>127715</td>      <th>  AIC:               </th> <td>-1.009e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>127714</td>      <th>  BIC:               </th> <td>-1.009e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>voi</th> <td>    0.0006</td> <td> 4.19e-06</td> <td>  142.543</td> <td> 0.000</td> <td>    0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>19934.966</td> <th>  Durbin-Watson:     </th>  <td>   2.548</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>264593.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.322</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>10.022</td>   <th>  Cond. No.          </th>  <td>    1.00</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              dmidprice   R-squared:                       0.137\n",
       "Model:                            OLS   Adj. R-squared:                  0.137\n",
       "Method:                 Least Squares   F-statistic:                 2.032e+04\n",
       "Date:                Tue, 14 Nov 2017   Prob (F-statistic):               0.00\n",
       "Time:                        09:09:57   Log-Likelihood:             5.0451e+05\n",
       "No. Observations:              127715   AIC:                        -1.009e+06\n",
       "Df Residuals:                  127714   BIC:                        -1.009e+06\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "voi            0.0006   4.19e-06    142.543      0.000       0.001       0.001\n",
       "==============================================================================\n",
       "Omnibus:                    19934.966   Durbin-Watson:                   2.548\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           264593.216\n",
       "Skew:                          -0.322   Prob(JB):                         0.00\n",
       "Kurtosis:                      10.022   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(df['dmidprice'], df['voi']).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7QAAAJCCAYAAADwe4seAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAgAA0KX/H2//xuzEOebdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252d1c85198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.voi, df.dmidprice);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second feature will be simple order book imbalance, whereby: $I = V_b - V_a $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['I'] = df['bidsize'] - df['asksize']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>bidprice</th>\n",
       "      <th>bidsize</th>\n",
       "      <th>askprice</th>\n",
       "      <th>asksize</th>\n",
       "      <th>midprice</th>\n",
       "      <th>dmidprice</th>\n",
       "      <th>voi</th>\n",
       "      <th>I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-04-18 00:00:01.661</td>\n",
       "      <td>39.78</td>\n",
       "      <td>20</td>\n",
       "      <td>39.80</td>\n",
       "      <td>5</td>\n",
       "      <td>39.790</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-04-18 00:00:01.664</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.80</td>\n",
       "      <td>8</td>\n",
       "      <td>39.795</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2016-04-18 00:00:02.801</td>\n",
       "      <td>39.78</td>\n",
       "      <td>19</td>\n",
       "      <td>39.80</td>\n",
       "      <td>18</td>\n",
       "      <td>39.790</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2016-04-18 00:00:02.801</td>\n",
       "      <td>39.78</td>\n",
       "      <td>14</td>\n",
       "      <td>39.79</td>\n",
       "      <td>1</td>\n",
       "      <td>39.785</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2016-04-18 00:00:02.805</td>\n",
       "      <td>39.78</td>\n",
       "      <td>12</td>\n",
       "      <td>39.80</td>\n",
       "      <td>20</td>\n",
       "      <td>39.790</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  bidprice  bidsize  askprice  asksize  midprice  \\\n",
       "8  2016-04-18 00:00:01.661     39.78       20     39.80        5    39.790   \n",
       "16 2016-04-18 00:00:01.664     39.79        1     39.80        8    39.795   \n",
       "40 2016-04-18 00:00:02.801     39.78       19     39.80       18    39.790   \n",
       "47 2016-04-18 00:00:02.801     39.78       14     39.79        1    39.785   \n",
       "49 2016-04-18 00:00:02.805     39.78       12     39.80       20    39.790   \n",
       "\n",
       "    dmidprice  voi   I  \n",
       "8      -0.005  0.0  15  \n",
       "16      0.005  1.0  -7  \n",
       "40     -0.005  0.0   1  \n",
       "47     -0.005 -1.0  13  \n",
       "49      0.005  0.0  -8  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>dmidprice</td>    <th>  R-squared:         </th>  <td>   0.408</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.408</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>8.819e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 14 Nov 2017</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:10:12</td>     <th>  Log-Likelihood:    </th> <td>5.2861e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>127715</td>      <th>  AIC:               </th> <td>-1.057e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>127714</td>      <th>  BIC:               </th> <td>-1.057e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I</th> <td>-9.338e-05</td> <td> 3.14e-07</td> <td> -296.972</td> <td> 0.000</td> <td> -9.4e-05</td> <td>-9.28e-05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>108.479</td> <th>  Durbin-Watson:     </th> <td>   2.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 111.791</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.058</td>  <th>  Prob(JB):          </th> <td>5.31e-25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.088</td>  <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              dmidprice   R-squared:                       0.408\n",
       "Model:                            OLS   Adj. R-squared:                  0.408\n",
       "Method:                 Least Squares   F-statistic:                 8.819e+04\n",
       "Date:                Tue, 14 Nov 2017   Prob (F-statistic):               0.00\n",
       "Time:                        09:10:12   Log-Likelihood:             5.2861e+05\n",
       "No. Observations:              127715   AIC:                        -1.057e+06\n",
       "Df Residuals:                  127714   BIC:                        -1.057e+06\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "I          -9.338e-05   3.14e-07   -296.972      0.000    -9.4e-05   -9.28e-05\n",
       "==============================================================================\n",
       "Omnibus:                      108.479   Durbin-Watson:                   2.001\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              111.791\n",
       "Skew:                          -0.058   Prob(JB):                     5.31e-25\n",
       "Kurtosis:                       3.088   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(df['dmidprice'], df['I']).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>dmidprice</td>    <th>  R-squared:         </th>  <td>   0.447</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.447</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>5.172e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 14 Nov 2017</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:10:15</td>     <th>  Log-Likelihood:    </th> <td>5.3297e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>127715</td>      <th>  AIC:               </th> <td>-1.066e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>127713</td>      <th>  BIC:               </th> <td>-1.066e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I</th>   <td>-8.485e-05</td> <td> 3.17e-07</td> <td> -267.787</td> <td> 0.000</td> <td>-8.55e-05</td> <td>-8.42e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>voi</th> <td>    0.0003</td> <td>  3.5e-06</td> <td>   94.963</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>467.736</td> <th>  Durbin-Watson:     </th> <td>   2.074</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 498.134</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.123</td>  <th>  Prob(JB):          </th> <td>6.78e-109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.183</td>  <th>  Cond. No.          </th> <td>    11.5</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              dmidprice   R-squared:                       0.447\n",
       "Model:                            OLS   Adj. R-squared:                  0.447\n",
       "Method:                 Least Squares   F-statistic:                 5.172e+04\n",
       "Date:                Tue, 14 Nov 2017   Prob (F-statistic):               0.00\n",
       "Time:                        09:10:15   Log-Likelihood:             5.3297e+05\n",
       "No. Observations:              127715   AIC:                        -1.066e+06\n",
       "Df Residuals:                  127713   BIC:                        -1.066e+06\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "I          -8.485e-05   3.17e-07   -267.787      0.000   -8.55e-05   -8.42e-05\n",
       "voi            0.0003    3.5e-06     94.963      0.000       0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                      467.736   Durbin-Watson:                   2.074\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              498.134\n",
       "Skew:                          -0.123   Prob(JB):                    6.78e-109\n",
       "Kurtosis:                       3.183   Cond. No.                         11.5\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(df['dmidprice'], df[['I', 'voi']]).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25284bfd710>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7QAAAJCCAYAAADwe4seAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x252849ac278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['I'], df['dmidprice'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
